Stress x EE MA
Supplementary Material
Setting-up
Loading packages
pacman::p_load(tidyverse,
here,
metafor,
clubSandwich,
orchaRd,
MuMIn,
patchwork,
GoodmanKruskal,
networkD3,
ggplot2,
visdat,
ggalluvial,
ggthemr,
cowplot)
# needed for model selection using MuMIn within metafor
eval(metafor:::.MuMIn)Loading data and functions
dat <- read_csv(here("Data","Data_raw.csv"))
# Load custom function to extract data
source(here("R/Functions.R")) Data exploration
General
#Number of effect sizes
length(unique(dat$ES_ID))
#Number of studies
length(unique(dat$Study_ID))
#Publication years
min(dat$Year_published)
max(dat$Year_published)Explore associations among predictor variables
plot_missing <- vis_miss(dat) +
theme(plot.title = element_text(hjust = 0.5, vjust = 3),
plot.margin = margin(t = 0.5, r = 3, b = 1, l = 1, unit = "cm")) +
ggtitle("Missing data in the selected predictors") #no missing values
plot_missing
#useGoodman and Kruskal’s τ measure of association between categorical predictor variables (function from package GoodmanKruskal: https://cran.r-project.org/web/packages/GoodmanKruskal/vignettes/GoodmanKruskal.html)
#GKmatrix <- GKtauDataframe(subset(dat, select = c("Sex", "Type_assay", "Learning_vs_memory", #"Type_reinforcement", "Type_stress_exposure", "Age_stress_exposure", "Stress_duration", #"EE_social_HR", "EE_exercise", "Age_EE_exposure", "Exposure_order", "Age_assay")))
#plot(GKmatrix)
#simple pairwise contingency tables
# table(dat$Type_assay, dat$Type_reinforcement)
# table(dat$Age_stress_exposure, dat$Age_EE_exposure)
# table(dat$Type_stress_exposure, dat$Age_stress_exposure)
# table(dat$Type_stress_exposure, dat$Age_assay)
# table(dat$Type_stress_exposure, dat$Stress_duration)Alluvial diagrams
#A. subjects info: species-strain-sex
freq_A <- as.data.frame(table(dat$Sex, dat$Common_species, dat$Strain)) %>% rename(Sex = Var1, Species = Var2, Strain = Var3) #make a data frame of frequencies for three selected variables
is_alluvia_form(as.data.frame(freq_A), axes = 1:3, silent = TRUE)
p1 <- ggplot(data = freq_A,
aes(axis1 = Sex, axis2 = Species, axis3 = Strain, y = Freq)) +
geom_alluvium(aes(fill = Sex)) +
geom_flow() +
geom_stratum(aes(fill = Sex)) +
geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
#theme_minimal() +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0, vjust = 3),
axis.title.x = element_text(),
axis.text.x = element_text(face="bold"),
plot.margin = unit(c(1, 1, 0, 1), "cm")) +
scale_x_discrete(limits = c("Sex", "Species", "Strain"), expand = c(0.15, 0.05), position = "top") +
ggtitle("A study subjects")
p1
#B. EE info: type-exercise-social EE
freq_B <- as.data.frame(table(dat$Type_EE_exposure, dat$EE_exercise, dat$EE_social)) %>% rename(Type_EE = Var1, EE_exercise = Var2, EE_social = Var3) #make a data frame of frequencies for three selected variables
is_alluvia_form(as.data.frame(freq_B), axes = 1:3, silent = TRUE)
p2 <- ggplot(data = freq_B,
aes(axis1 = Type_EE, axis2 = EE_exercise, axis3 = EE_social, y = Freq)) +
geom_alluvium(aes(fill = Type_EE)) +
geom_flow() +
geom_stratum(aes(fill = Type_EE)) +
geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
#theme_minimal() +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0, vjust = 3),
axis.title.x = element_text(),
axis.text.x = element_text(face="bold"),
plot.margin = unit(c(1, 1, 0, 1), "cm")) +
scale_x_discrete(limits = c("Type", "Exercise", "Social"), expand = c(0.1, 0.1), position = "top") +
ggtitle("C environmental enrichment")
p2
#C. stress info: age-duration-type stress
freq_C <- as.data.frame(table(dat$Age_stress_exposure, dat$Stress_duration, dat$Type_stress_exposure)) %>% rename(Age_stress = Var1, Duration_stress = Var2, Type_stress = Var3) #make a data frame of frequencies for three selected variables
is_alluvia_form(as.data.frame(freq_C), axes = 1:3, silent = TRUE)
p3 <- ggplot(data = freq_C,
aes(axis1 = Age_stress, axis2 = Duration_stress, axis3 = Type_stress, y = Freq)) +
geom_alluvium(aes(fill = Age_stress)) +
geom_flow() +
geom_stratum(aes(fill = Age_stress)) +
geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
#theme_minimal() +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0, vjust = 3),
axis.title.x = element_text(),
axis.text.x = element_text(face="bold"),
plot.margin = unit(c(1, 1, 0, 1), "cm")) +
scale_x_discrete(limits = c("Age", "Duration", "Type"), expand = c(0.1, 0.1), position = "top") +
ggtitle("B stress exposure")
p3
#D. assay info: L/M-type-reinforcement
freq_D <- as.data.frame(table(dat$Learning_vs_memory, dat$Type_assay, dat$Type_reinforcement)) %>% rename(Learning_Memory = Var1, Type = Var2, Reinforcement = Var3) #make a data frame of frequencies for three selected variables
is_alluvia_form(as.data.frame(freq_D), axes = 1:3, silent = TRUE)
p4 <- ggplot(data = freq_D,
aes(axis1 = Learning_Memory, axis2 = Type, axis3 = Reinforcement, y = Freq)) +
geom_alluvium(aes(fill = Learning_Memory)) +
geom_flow() +
geom_stratum(aes(fill = Learning_Memory)) +
geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
#theme_minimal() +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0, vjust = 3),
axis.title.x = element_text(),
axis.text.x = element_text(face="bold"),
plot.margin = unit(c(1, 1, 0, 1), "cm")) +
scale_x_discrete(limits = c("Learning_Memory", "Type", "Reinforcement"), expand = c(0.1, 0.1), position = "top") +
ggtitle("D cognitive assay")
p4
#p1 + scale_fill_brewer(palette = "Set3") #Pastel1
(p1 + scale_fill_brewer(palette = "Set3")) / (p2 + scale_fill_brewer(palette = "Set3")) / (p3 + scale_fill_brewer(palette = "Set3")) / (p4 + scale_fill_brewer(palette = "Set3")) + plot_layout(ncol = 1, heights = c(1,1,1,1,1))
#ggsave(file = "./figs/Alluvial_diagrams_v0.pdf", width = 10, height = 12, units = "cm", dpi = 300, scale = 2, device = cairo_pdf)Data organisation
Removing study with negative values, getting effect sizes from function, ‘flipping’ effect sizes so that all effect sizes are higher values = individuals do better and learning/memory, shifting negative values to positive as lnRR cannot use negative values, assigining human readable terms, and creating VCV of variance
#this code is to process the data using "Data_raw". Processed data after this step was loaded above
#removing study with negative values as these are unable to be used for lnRR
dat <- droplevels(dat[!dat$First_author == 'Wang',])
# #Getting effect sizes
# effect_size <- effect_set(CC_n = "CC_n", CC_mean = "CC_mean", CC_SD = "CC_SD",
# EC_n = "EC_n", EC_mean = "EC_mean" , EC_SD ="EC_SD",
# CS_n = "CS_n", CS_mean = "CS_mean", CS_SD = "CS_SD",
# ES_n = "ES_n", ES_mean = "ES_mean", ES_SD = "ES_SD",
# data = dat)
# #'pure' effect sizes
# effect_size2 <- effect_set2(CC_n = "CC_n", CC_mean = "CC_mean", CC_SD = "CC_SD",
# EC_n = "EC_n", EC_mean = "EC_mean" , EC_SD ="EC_SD",
# CS_n = "CS_n", CS_mean = "CS_mean", CS_SD = "CS_SD",
# ES_n = "ES_n", ES_mean = "ES_mean", ES_SD = "ES_SD",
# data = dat)
#rounding down integer sample sizes
dat$CC_n <- floor(dat$CC_n)
dat$EC_n <- floor(dat$EC_n)
dat$CS_n <- floor(dat$CS_n)
dat$ES_n <- floor(dat$CS_n)
# 'Focal' effect_size
effect_size <- with(dat, mapply(effect_set,
CC_n ,
CC_mean,
CC_SD,
EC_n,
EC_mean,
EC_SD,
CS_n,
CS_mean,
CS_SD,
ES_n,
ES_mean,
ES_SD,
percent = Response_percent,
SIMPLIFY = FALSE))
effect_size <- map_dfr(effect_size, I)
# 'Pairwise' effect size
effect_size2 <- with(dat, mapply(effect_set2,
CC_n ,
CC_mean,
CC_SD,
EC_n,
EC_mean,
EC_SD,
CS_n,
CS_mean,
CS_SD,
ES_n,
ES_mean,
ES_SD,
percent = Response_percent,
SIMPLIFY = FALSE))
effect_size2 <- map_dfr(effect_size2, I)
#Removing missing effect sizes
dim(dat)## [1] 92 73full_info <- which(complete.cases(effect_size) == TRUE)
# adding effect sizes as column
dat <- bind_cols(dat, effect_size, effect_size2)
dat <- dat[full_info, ]
#Flipping 'lower is better' effect sizes
#flipping lnRR for values where higher = worse
dat$lnRR_Ea <- ifelse(dat$Response_direction == 2, dat$lnRR_E*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_E))
# currently NAswhich causes error
dat$lnRR_Sa <- ifelse(dat$Response_direction == 2, dat$lnRR_S*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_S)) # currently NAswhich causes error
dat$lnRR_ESa <- ifelse(dat$Response_direction == 2, dat$lnRR_ES*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_ES)) # currently NAswhich causes error
#flipping 'pure effect sizes'
dat$lnRR_E2a <- ifelse(dat$Response_direction == 2, dat$lnRR_E2*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_E2)) # currently NAswhich causes error
dat$lnRR_S2a <- ifelse(dat$Response_direction == 2, dat$lnRR_S2*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_S2)) # currently NAswhich causes error
dat$lnRR_ES2a <- ifelse(dat$Response_direction == 2, dat$lnRR_ES2*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_ES2)) # currently NAswhich causes error
dat$lnRR_E3a <- ifelse(dat$Response_direction == 2, dat$lnRR_E3*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_E3)) # currently NAswhich causes error
dat$lnRR_S3a <- ifelse(dat$Response_direction == 2, dat$lnRR_S3*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_S3)) # currently NAswhich causes error
#flipping SMD
dat$SMD_Ea <- ifelse(dat$Response_direction == 2, dat$SMD_E*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_E)) # currently NAswhich causes error
dat$SMD_Sa <- ifelse(dat$Response_direction == 2, dat$SMD_S*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_S)) # currently NAswhich causes error
dat$SMD_ESa <- ifelse(dat$Response_direction == 2, dat$SMD_ES*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_ES))
#assigning human readable terms
dat <- dat %>% mutate(Type_assay = case_when(Type_assay == 1 ~ "Habituation",
Type_assay == 2 ~ "Conditioning",
Type_assay == 3 ~ "Recognition",
Type_assay == 4 ~ "Unclear"),
Learning_vs_memory = case_when(Learning_vs_memory == 1 ~ "Learning",
Learning_vs_memory == 2 ~ "Memory",
Learning_vs_memory == 3 ~ "Habituation"),
Type_reinforcement = case_when(Type_reinforcement== 1 ~"Appetitive",
Type_reinforcement== 2 ~ "Aversive",
Type_reinforcement== 3 ~ "Not applicable",
Type_reinforcement== 4 ~ "Unclear"),
Type_stress_exposure = case_when(Type_stress_exposure == 1 ~ "Density",
Type_stress_exposure == 2 ~ "Scent",
Type_stress_exposure == 3 ~ "Shock",
Type_stress_exposure == 4 ~ "Exertion",
Type_stress_exposure == 5 ~ "Restraint",
Type_stress_exposure == 6 ~ "MS",
Type_stress_exposure == 7 ~ "Circadian rhythm",
Type_stress_exposure == 8 ~ "Noise",
Type_stress_exposure == 9 ~ "Other",
Type_stress_exposure == 10 ~ "Combination",
Type_stress_exposure == 11 ~ "unclear"),
Age_stress_exposure = case_when(Age_stress_exposure == 1 ~ "Prenatal",
Age_stress_exposure == 2 ~ "Early postnatal",
Age_stress_exposure == 3 ~ "Adolescent",
Age_stress_exposure == 4 ~ "Adult",
Age_stress_exposure == 5 ~ "Unclear"),
Stress_duration = case_when(Stress_duration == 1 ~ "Acute",
Stress_duration == 2 ~ "Chronic",
Stress_duration == 3 ~ "Intermittent",
Stress_duration == 4 ~ "Unclear"),
EE_social = case_when(EE_social == 1 ~ "Social",
EE_social== 2 ~ "Non-social",
EE_social == 3 ~ "Unclear"),
EE_exercise = case_when(EE_exercise == 1 ~ "Exercise",
EE_exercise == 2 ~ "No exercise"),
Age_EE_exposure = case_when(Age_EE_exposure == 1 ~ "Prenatal",
Age_EE_exposure == 2 ~ "Early postnatal",
Age_EE_exposure == 3 ~ "Adolescent",
Age_EE_exposure == 4 ~ "Adult",
Age_EE_exposure == 5 ~ "Unclear"),
Exposure_order = case_when(Exposure_order == 1 ~ "Stress first",
Exposure_order == 2 ~ "Enrichment first",
Exposure_order == 3 ~ "Concurrently",
Exposure_order == 4 ~ "Unclear"),
Age_assay = case_when(Age_assay == 1 ~ "Early postnatal",
Age_assay == 2 ~ "Adolescent",
Age_assay == 3 ~ "Adult",
Age_assay == 4 ~ "Unclear"),
Sex = case_when(Sex == 1 ~ "Female",
Sex == 2 ~ "Male",
Sex == 3 ~ "Mixed",
Sex == 4 ~ "Unclear"),
Type_EE_exposure = case_when(Type_EE_exposure == 1 ~ "Nesting material",
Type_EE_exposure == 2 ~ "Objects",
Type_EE_exposure == 3 ~ "Cage complexity",
Type_EE_exposure == 4 ~ "Wheel/trademill",
Type_EE_exposure == 5 ~ "Combination",
Type_EE_exposure == 6 ~ "Other",
Type_EE_exposure == 7 ~ "Unclear"))
#making variance VCVs
VCV_E <- impute_covariance_matrix(vi = dat$lnRRV_E, cluster = dat$Study_ID, r = 0.5)
VCV_S <- impute_covariance_matrix(vi = dat$lnRRV_S, cluster = dat$Study_ID, r = 0.5)
VCV_ES <- impute_covariance_matrix(vi = dat$lnRRV_ES, cluster = dat$Study_ID, r = 0.5)
VCV_Ea <- impute_covariance_matrix(vi = dat$SMDV_E, cluster = dat$Study_ID, r = 0.5)
VCV_Sa <- impute_covariance_matrix(vi = dat$SMDV_S, cluster = dat$Study_ID, r = 0.5)
VCV_ESa <- impute_covariance_matrix(vi = dat$SMDV_ES, cluster = dat$Study_ID, r = 0.5)
#write.csv(dat, file = here("Data", 'Data_processed.csv'), row.names = TRUE)Modelling with lnRR
Environmental enrichment
Meta-analysis
Learning and memory are significantly improved when housed with environmnetal enrichment
#dat <- read_csv(here("Data","Data_processed.csv"))
mod_E0 <- rma.mv(yi = lnRR_Ea, V = VCV_E, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_E0) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -9.7836 19.5672 27.5672 37.6106 28.0323
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0082 0.0905 30 no Study_ID
## sigma^2.2 0.0345 0.1858 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Heterogeneity:
## Q(df = 91) = 809.2712, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.1860 0.0320 5.8116 91 <.0001 0.1224 0.2496 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_E0) ## I2_total I2_Study_ID I2_ES_ID I2_Strain
## 9.310673e-01 1.786605e-01 7.524068e-01 6.097041e-10orchard_plot(mod_E0, mod = "Int", xlab = "lnRR", alpha=0.4) + # Orchard plot
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2)+ # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_colour_manual(values = "darkorange")+ # change colours
scale_fill_manual(values="darkorange")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Meta-regression: uni-moderator
Type of assay
The type of learning/memory response
dat1 <- filter(dat, Type_assay %in% c("Recognition", "Habituation", "Conditioning"))
VCV_E1 <- impute_covariance_matrix(vi = dat1$lnRRV_E, cluster = dat$Study_ID, r = 0.5)
mod_E1 <- rma.mv(yi = lnRR_Ea, V = VCV_E1, mod = ~Type_assay-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat1)
summary(mod_E1)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -7.8496 15.6993 27.6993 42.6311 28.7237
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0099 0.0995 30 no Study_ID
## sigma^2.2 0.0321 0.1792 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 89) = 661.8903, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 89) = 12.7993, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## Type_assayConditioning 0.2167 0.0351 6.1650 89 <.0001 0.1468 0.2865
## Type_assayHabituation 0.1821 0.1147 1.5886 89 0.1157 -0.0457 0.4100
## Type_assayRecognition 0.0554 0.0640 0.8659 89 0.3889 -0.0717 0.1826
##
## Type_assayConditioning ***
## Type_assayHabituation
## Type_assayRecognition
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E1) ## R2_marginal R2_coditional
## 0.07376134 1.00000000Learning_E <- orchard_plot(mod_E1, mod = "Type_assay", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
xlim(-0.5, 2) +
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
Learning_E
Learning vs Memory
Is the assay broadly measuring learning or memory?
mod_E2 <- rma.mv(yi = lnRR_Ea, V = VCV_E, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_E2) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -9.3793 18.7586 28.7586 41.2577 29.4729
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0076 0.0873 30 no Study_ID
## sigma^2.2 0.0340 0.1843 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 90) = 802.5794, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 90) = 18.2861, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Learning_vs_memoryLearning 0.2303 0.0450 5.1227 90 <.0001 0.1410
## Learning_vs_memoryMemory 0.1624 0.0355 4.5713 90 <.0001 0.0919
## ci.ub
## Learning_vs_memoryLearning 0.3197 ***
## Learning_vs_memoryMemory 0.2330 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E2) ## R2_marginal R2_coditional
## 0.02572583 1.00000000LvsM_E<- orchard_plot(mod_E2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
xlim(-0.5, 2) +
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
LvsM_E
Type of reinforcement
The type of cue used
dat2 <- filter(dat, Type_reinforcement %in% c("Appetitive", "Aversive", "Not applicable"))
VCV_E2 <- impute_covariance_matrix(vi = dat2$lnRRV_E, cluster = dat2$Study_ID, r = 0.5)
mod_E3 <- rma.mv(yi = lnRR_Ea, V = VCV_E2, mod = ~ Type_reinforcement-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat2)
summary(mod_E3)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -7.8702 15.7405 27.7405 42.6723 28.7649
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0109 0.1046 30 no Study_ID
## sigma^2.2 0.0319 0.1787 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 89) = 764.5984, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 89) = 12.4138, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_reinforcementAppetitive 0.2175 0.0726 2.9942 89 0.0036 0.0732
## Type_reinforcementAversive 0.2191 0.0410 5.3456 89 <.0001 0.1377
## Type_reinforcementNot applicable 0.0812 0.0560 1.4504 89 0.1505 -0.0300
## ci.ub
## Type_reinforcementAppetitive 0.3618 **
## Type_reinforcementAversive 0.3006 ***
## Type_reinforcementNot applicable 0.1924
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E3) ## R2_marginal R2_coditional
## 0.07720103 1.00000000Reinforcement_E <-orchard_plot(mod_E3, mod = "Type_reinforcement", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
xlim(-0.5, 2) +
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
Reinforcement_E
Exercise enrichment
Does the form of enrichment include exercise through a running wheel or treadmill?
mod_E5<- rma.mv(yi = lnRR_Ea, V = VCV_E, mod = ~EE_exercise-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_E5)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -10.0303 20.0606 30.0606 42.5597 30.7749
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0096 0.0980 30 no Study_ID
## sigma^2.2 0.0345 0.1857 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 90) = 807.7169, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 90) = 16.1666, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## EE_exerciseExercise 0.1849 0.0407 4.5464 90 <.0001 0.1041 0.2657
## EE_exerciseNo exercise 0.1900 0.0556 3.4151 90 0.0010 0.0795 0.3005
##
## EE_exerciseExercise ***
## EE_exerciseNo exercise ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E5) ## R2_marginal R2_coditional
## 0.0001360993 0.9999999987Exercise_E <-orchard_plot(mod_E5, mod = "EE_exercise", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
xlim(-0.5, 2) +
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
Exercise_E
Age of enrichment
The age at which the individuals were exposed to environmental enrichment.
dat6 <- filter(dat, Age_EE_exposure %in% c("Adult", "Adolescent"))
VCV_E6 <- impute_covariance_matrix(vi = dat6$lnRRV_E, cluster = dat6$Study_ID, r = 0.5)
mod_E6 <- rma.mv(yi = lnRR_Ea, V = VCV_E6, mod = ~Age_EE_exposure-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat6)
summary(mod_E6) ##
## Multivariate Meta-Analysis Model (k = 88; method: REML)
##
## logLik Deviance AIC BIC AICc
## -6.9490 13.8980 23.8980 36.1698 24.6480
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0078 0.0885 29 no Study_ID
## sigma^2.2 0.0327 0.1808 88 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 86) = 782.6092, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 86) = 18.9060, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## Age_EE_exposureAdolescent 0.1799 0.0370 4.8553 86 <.0001 0.1062 0.2535
## Age_EE_exposureAdult 0.2340 0.0620 3.7734 86 0.0003 0.1107 0.3573
##
## Age_EE_exposureAdolescent ***
## Age_EE_exposureAdult ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E6) ## R2_marginal R2_coditional
## 0.01127347 1.00000000Age_E<- orchard_plot(mod_E6, mod = "Age_EE_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
xlim(-0.5, 2) +
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
Age_E
Meta-regression: multi-moderator model
# filter data so that all K < 5 are removed
dat_Efm <- dat %>%
filter(Type_assay %in% c("Recognition", "Habituation", "Conditioning"),
Type_reinforcement %in% c("Appetitive", "Aversive", "Not applicable"),
EE_social %in% c("Social", "Non-social"),
Age_EE_exposure %in% c("Adult", "Adolescent"))
VCV_Efm <- impute_covariance_matrix(vi = dat_Efm$lnRRV_E, cluster = dat_Efm$Study_ID, r = 0.5)
mod_Efm <- rma.mv(yi = lnRR_Sa, V = VCV_Efm, mod = ~Type_assay-1 + Learning_vs_memory + Type_reinforcement + EE_social + EE_exercise + Age_EE_exposure , random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat_Efm)
#summary(mod_Efm)
#r2_ml(mod_Efm)
res_Efm <- dredge(mod_Efm, trace=2)
saveRDS(res_Efm, file = here("Rdata", "res_Efm.rds"))
# also saving the full model and data
saveRDS(mod_Efm, file = here("Rdata", "mod_Efm.rds"))
saveRDS(dat_Efm, file = here("Rdata", "dat_Efm.rds"))dat_Efm <- readRDS(file = here("Rdata", "dat_Efm.rds"))
mod_Efm <- readRDS(file = here("Rdata", "mod_Efm.rds"))
res_Efm <- readRDS(file = here("Rdata", "res_Efm.rds"))
res_Efm2<- subset(res_Efm, delta <= 6, recalc.weights=FALSE)
importance(res_Efm2)## Age_EE_exposure Type_assay EE_exercise EE_social
## Sum of weights: 0.63 0.36 0.16 0.14
## N containing models: 11 10 5 5
## Learning_vs_memory Type_reinforcement
## Sum of weights: 0.10 0.07
## N containing models: 4 4Publication bias & sensitivity analysis
Publication bias tests
# funnel plot
Funnel_E<-funnel(mod_Efm, xlab = "lnRR", ylab = "Standard Error")
Funnel_E## x y slab
## 1 -0.213497315 0.2433196 1
## 2 -0.173536758 0.3659399 2
## 3 -0.276500151 0.3944014 3
## 4 -0.102871039 0.2917889 4
## 5 -0.431860581 0.2607712 5
## 6 -0.220765234 0.3358620 6
## 7 0.123313002 0.2903953 7
## 8 -0.130681176 0.2203808 8
## 9 -0.377176406 0.2025027 9
## 10 -0.254349014 0.2677791 10
## 11 0.454080733 0.2772217 11
## 12 -0.561458736 0.3476971 12
## 13 0.673795583 0.3220059 13
## 14 -0.484338897 0.2614931 14
## 15 -0.424367932 0.2470021 15
## 16 -0.679891992 0.2875585 16
## 17 -0.330715414 0.2635523 17
## 18 -0.001942387 0.2036091 18
## 19 0.067763438 0.2030761 19
## 20 0.038435858 0.2546488 20
## 21 0.107274934 0.2004581 21
## 22 0.134554017 0.1891676 22
## 23 0.077353257 0.1941812 23
## 24 0.069783755 0.1959847 24
## 25 -0.147595466 0.1654517 25
## 26 -0.028117212 0.2036493 26
## 27 0.009803768 0.2086964 27
## 28 0.025882437 0.2246031 28
## 29 0.310018621 0.2270771 29
## 30 -0.221075048 0.2370404 30
## 31 -0.021333180 0.2042171 31
## 32 -0.024739571 0.2039836 32
## 33 0.024018193 0.2070339 33
## 34 0.055386135 0.2082554 34
## 35 0.011204912 0.2118017 35
## 36 0.269761044 0.2234418 36
## 37 -0.027431091 0.2616730 37
## 38 0.097383495 0.2201198 38
## 39 0.129821024 0.2274078 39
## 40 0.117013214 0.2118235 40
## 41 0.134604274 0.2121146 41
## 42 0.131395595 0.1826574 42
## 43 0.199838051 0.1848095 43
## 44 0.163896645 0.1854228 44
## 45 0.232145273 0.1907385 45
## 46 0.201251496 0.1853235 46
## 47 0.115420490 0.1933631 47
## 48 0.191292338 0.1971748 48
## 49 0.025102696 0.2057490 49
## 50 -0.090383649 0.2041439 50
## 51 0.063283209 0.1681331 51
## 52 0.404796892 0.2138700 52
## 53 0.496027289 0.2117179 53
## 54 -0.258236598 0.2166538 54
## 55 -0.203668502 0.2234975 55
## 56 -0.095266297 0.2026806 56
## 57 -0.127501308 0.2558545 57
## 58 -0.574954185 0.3729284 58
## 59 -0.201834800 0.2182224 59
## 60 0.238276751 0.2681091 60
## 61 0.474869293 0.4008041 61
## 62 -0.696237252 0.4468660 62
## 63 -0.432296145 0.2320240 63
## 64 -0.371862448 0.3217018 64
## 65 0.184084317 0.3001181 65
## 66 -0.360884079 0.3144041 66
## 67 -0.335056871 0.1984419 67
## 68 -0.201138369 0.1967313 68
## 69 -0.035550172 0.2030628 69
## 70 -0.102430545 0.3971746 70
## 71 0.508752824 0.2946114 71
## 72 0.438694650 0.2782131 72
## 73 0.467023121 0.3276902 73
## 74 -0.206361779 0.2538102 74
## 75 -0.092873206 0.2153932 75
## 76 0.064099073 0.2602010 76
## 77 -0.031175240 0.2103472 77
## 78 -0.217869097 0.2137524 78
## 79 -0.082144895 0.2104206 79
## 80 0.015058876 0.3001287 80
## 81 0.130606403 0.1865150 81
## 82 0.311232540 0.4672652 82
## 83 -0.162882220 0.2247811 83
## 84 0.289463536 0.5530860 84
## 85 0.345157695 0.2079106 85
## 86 -1.277954082 0.7053474 86
## 87 0.024867129 0.2317755 87
## 88 0.107414213 0.3971615 88#year published was scaled previously under stress PB
dat_Efm$sqrt_inv_e_n <- with(dat_Efm, sqrt(1/CC_n + 1/EC_n + 1/ES_n + 1/CS_n))
PB_MR_E<- rma.mv(lnRR_Sa, lnRRV_S, mods = ~1 + sqrt_inv_e_n + Learning_vs_memory + Year_published + Type_assay + Type_reinforcement + EE_social + EE_exercise + Age_stress_exposure, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain), method = "REML", test = "t",
data = dat_Efm)
estimates_PB_MR_E<- estimates.CI(PB_MR_E)
estimates_PB_MR_E## estimate mean lower upper
## 1 intrcpt -8.327518592 -48.07119985 31.41616266
## 2 sqrt_inv_e_n -0.109285094 -0.87720366 0.65863347
## 3 Learning_vs_memoryMemory -0.069013676 -0.16279041 0.02476306
## 4 Year_published 0.004107572 -0.01566019 0.02387534
## 5 Type_assayHabituation -0.581287761 -0.96454156 -0.19803397
## 6 Type_assayRecognition -0.138892812 -0.43927047 0.16148484
## 7 Type_reinforcementAversive 0.180286955 -0.12948049 0.49005440
## 8 Type_reinforcementNot applicable 0.347157232 -0.07216803 0.76648249
## 9 EE_socialSocial 0.049422109 -0.16084227 0.25968649
## 10 EE_exerciseNo exercise -0.040127169 -0.27510528 0.19485094
## 11 Age_stress_exposureAdult -0.164507296 -0.53782472 0.20881013
## 12 Age_stress_exposureEarly postnatal -0.070780676 -0.45223223 0.31067088
## 13 Age_stress_exposurePrenatal -0.142070893 -0.53156091 0.24741912#no effect of inv_effective sample size or year publishedLeave-one-out analysis
dat$Study_ID <- as.factor(dat$Study_ID)
LeaveOneOut_effectsize <- list()
for(i in 1:length(levels(dat$Study_ID))){
LeaveOneOut_effectsize[[i]] <- rma.mv(yi = lnRR_Ea, V = lnRRV_E,
random = list(~1 | Study_ID,~1| ES_ID, ~1 | Strain),
method = "REML", data = dat[dat$Study_ID
!= levels(dat$Study_ID)[i], ])}
# writing function for extracting est, ci.lb, and ci.ub from all models
est.func <- function(mod_E0){
df <- data.frame(est = mod_E0$b, lower = mod_E0$ci.lb, upper = mod_E0$ci.ub)
return(df)
}
#using dplyr to form data frame
MA_CVR_E <- lapply(LeaveOneOut_effectsize, function(x) est.func(x))%>% bind_rows %>% mutate(left_out = levels(dat$Study_ID))
saveRDS(MA_CVR_E,file = here("Rdata", "MA_CVR_E.rds"))#telling ggplot to stop reordering factors
MA_CVR_E <- readRDS(file = here("Rdata", "MA_CVR_E.rds"))
MA_CVR_E$left_out<- as.factor(MA_CVR_E$left_out)
MA_CVR_E$left_out<-factor(MA_CVR_E$left_out, levels = MA_CVR_E$left_out)
#plotting
leaveoneout_E <- ggplot(MA_CVR_E) +
geom_hline(yintercept = 0, lty = 2, lwd = 1) +
geom_hline(yintercept = mod_E0$ci.lb, lty = 3, lwd = 0.75, colour = "black") +
geom_hline(yintercept = mod_E0$b, lty = 1, lwd = 0.75, colour = "black") +
geom_hline(yintercept = mod_E0$ci.ub, lty = 3, lwd = 0.75, colour = "black") +
geom_pointrange(aes(x = left_out, y = est, ymin = lower, ymax = upper)) +
xlab("Study left out") +
ylab("lnRR, 95% CI") +
coord_flip() +
theme(panel.grid.minor = element_blank())+
theme_bw() + theme(panel.grid.major = element_blank()) +
theme(panel.grid.minor.x = element_blank() ) +
theme(axis.text.y = element_text(size = 6))
leaveoneout_E
dat$Study_ID <- as.integer(dat$Study_ID)Stress
Intercept model
Learning and memory are significantly reduced due to stress. High heterogeneity
mod_S0 <- rma.mv(yi = lnRR_Sa, V = VCV_S, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_S0) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -14.1156 28.2312 36.2312 46.2747 36.6964
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0099 0.0993 30 no Study_ID
## sigma^2.2 0.0377 0.1941 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Heterogeneity:
## Q(df = 91) = 946.9234, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.1052 0.0337 -3.1217 91 0.0024 -0.1721 -0.0383 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_S0) ## I2_total I2_Study_ID I2_ES_ID I2_Strain
## 9.376124e-01 1.946168e-01 7.429955e-01 1.975455e-10orchard_plot(mod_S0, mod = "Int", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Meta-regression
Uni-moderator metaregression
Type of assay
The type of learning/memory response
dat$Type_assay<-as.factor(dat$Type_assay)
VCV_S1 <- impute_covariance_matrix(vi = dat1$lnRRV_S, cluster = dat$Study_ID, r = 0.5)
mod_S1 <- rma.mv(yi = lnRR_Sa, V = VCV_S1, mod = ~Type_assay-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat1)
summary(mod_S1)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -9.8028 19.6055 31.6055 46.5374 32.6299
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0161 0.1271 30 no Study_ID
## sigma^2.2 0.0279 0.1671 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 89) = 723.4973, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 89) = 6.7053, p-val = 0.0004
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## Type_assayConditioning -0.0981 0.0375 -2.6192 89 0.0104 -0.1725 -0.0237
## Type_assayHabituation -0.4615 0.1126 -4.0969 89 <.0001 -0.6853 -0.2377
## Type_assayRecognition -0.0534 0.0645 -0.8287 89 0.4095 -0.1816 0.0747
##
## Type_assayConditioning *
## Type_assayHabituation ***
## Type_assayRecognition
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S1) ## R2_marginal R2_coditional
## 0.1853359 1.0000000Learning_S <-orchard_plot(mod_S1, mod = "Type_assay", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
Learning_S
Learning vs Memory
Is the assay broadly measuring learning or memory?
mod_S2 <- rma.mv(yi = lnRR_Sa, V = VCV_S, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_S2) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -14.5281 29.0562 39.0562 51.5553 39.7705
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0094 0.0970 30 no Study_ID
## sigma^2.2 0.0388 0.1969 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 90) = 946.3930, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 90) = 5.0145, p-val = 0.0086
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Learning_vs_memoryLearning -0.1211 0.0476 -2.5423 90 0.0127 -0.2157
## Learning_vs_memoryMemory -0.0974 0.0380 -2.5648 90 0.0120 -0.1728
## ci.ub
## Learning_vs_memoryLearning -0.0265 *
## Learning_vs_memoryMemory -0.0219 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S2) ## R2_marginal R2_coditional
## 0.002776034 1.000000000LvsM_S <- orchard_plot(mod_S2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
LvsM_S 
Type of reinforcement
The type of cue used
VCV_S2 <- impute_covariance_matrix(vi = dat2$lnRRV_S, cluster = dat$Study_ID, r = 0.5)
mod_S3 <- rma.mv(yi = lnRR_Sa, V = VCV_S2, mod = ~ Type_reinforcement-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat2)
summary(mod_S3)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -13.8810 27.7621 39.7621 54.6939 40.7864
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0103 0.1016 30 no Study_ID
## sigma^2.2 0.0387 0.1966 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 89) = 920.8439, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 89) = 3.7942, p-val = 0.0130
##
## Model Results:
##
## estimate se tval df pval
## Type_reinforcementAppetitive -0.1846 0.0749 -2.4649 89 0.0156
## Type_reinforcementAversive -0.0730 0.0427 -1.7081 89 0.0911
## Type_reinforcementNot applicable -0.1172 0.0590 -1.9851 89 0.0502
## ci.lb ci.ub
## Type_reinforcementAppetitive -0.3334 -0.0358 *
## Type_reinforcementAversive -0.1579 0.0119 .
## Type_reinforcementNot applicable -0.2345 0.0001 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S3) ## R2_marginal R2_coditional
## 0.0366428 1.0000000Reinforcement_S <-orchard_plot(mod_S3, mod = "Type_reinforcement", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
Reinforcement_S
Type of stress
The type of stress manipulation
dat3 <- filter(dat, Type_stress_exposure %in% c("Restraint", "Noise", "MS", "Combination"))
VCV_S3 <- impute_covariance_matrix(vi = dat3$lnRRV_S, cluster = dat3$Study_ID, r = 0.5)
mod_S4 <- rma.mv(yi = lnRR_Sa, V = VCV_S3, mod = ~Type_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat3)
summary(mod_S4) ##
## Multivariate Meta-Analysis Model (k = 85; method: REML)
##
## logLik Deviance AIC BIC AICc
## -11.4138 22.8276 36.8276 53.5887 38.3618
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0115 0.1071 25 no Study_ID
## sigma^2.2 0.0401 0.2002 85 no ES_ID
## sigma^2.3 0.0000 0.0000 4 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 81) = 897.4553, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 81) = 2.8767, p-val = 0.0278
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_stress_exposureCombination -0.0500 0.0892 -0.5605 81 0.5767 -0.2274
## Type_stress_exposureMS -0.0539 0.0560 -0.9630 81 0.3384 -0.1653
## Type_stress_exposureNoise -0.1203 0.1036 -1.1608 81 0.2491 -0.3265
## Type_stress_exposureRestraint -0.2101 0.0704 -2.9863 81 0.0037 -0.3501
## ci.ub
## Type_stress_exposureCombination 0.1274
## Type_stress_exposureMS 0.0575
## Type_stress_exposureNoise 0.0859
## Type_stress_exposureRestraint -0.0701 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S4)## R2_marginal R2_coditional
## 0.07029554 1.00000000Stressor<- orchard_plot(mod_S4, mod = "Type_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
Stressor
Age of stress
VCV_S3a <- impute_covariance_matrix(vi = dat$lnRRV_S, cluster = dat$Study_ID, r = 0.5)
mod_S5 <-rma.mv(yi = lnRR_Sa, V = VCV_S3a, mod = ~Age_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_S5) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -12.4083 24.8166 38.8166 56.1579 40.2166
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0048 0.0694 30 no Study_ID
## sigma^2.2 0.0392 0.1979 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 881.1229, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 4.3703, p-val = 0.0029
##
## Model Results:
##
## estimate se tval df pval
## Age_stress_exposureAdolescent 0.0074 0.1159 0.0641 88 0.9490
## Age_stress_exposureAdult -0.2279 0.0622 -3.6664 88 0.0004
## Age_stress_exposureEarly postnatal -0.0561 0.0435 -1.2891 88 0.2007
## Age_stress_exposurePrenatal -0.1145 0.0743 -1.5404 88 0.1271
## ci.lb ci.ub
## Age_stress_exposureAdolescent -0.2228 0.2377
## Age_stress_exposureAdult -0.3514 -0.1044 ***
## Age_stress_exposureEarly postnatal -0.1425 0.0304
## Age_stress_exposurePrenatal -0.2621 0.0332
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S5) ## R2_marginal R2_coditional
## 0.09987307 1.00000000Age_S <- orchard_plot(mod_S5, mod = "Age_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
Age_S 
Stess duration
How long was the stress applied for (chronic = every day for 7 days or more)? This has the highest marginal R2
dat4 <- filter(dat, Stress_duration %in% c("Chronic", "Acute"))
VCV_S4 <- impute_covariance_matrix(vi = dat4$lnRRV_S, cluster = dat4$Study_ID, r = 0.5)
mod_S6 <-rma.mv(yi = lnRR_Sa, V = VCV_S4, mod = ~Stress_duration-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat4)
summary(mod_S6) ##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -13.7898 27.5796 37.5796 49.9092 38.3204
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0062 0.0786 29 no Study_ID
## sigma^2.2 0.0391 0.1978 89 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 87) = 915.9393, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 87) = 6.6661, p-val = 0.0020
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## Stress_durationAcute 0.0135 0.0659 0.2045 87 0.8384 -0.1176 0.1445
## Stress_durationChronic -0.1360 0.0373 -3.6456 87 0.0005 -0.2101 -0.0618
##
## Stress_durationAcute
## Stress_durationChronic ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S6) ## R2_marginal R2_coditional
## 0.08245896 1.00000000Duration_S <- orchard_plot(mod_S6, mod = "Stress_duration", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
Duration_S 
Meta-regression: multi-moderator model
#selecting moderator levels with k >=5
dat_Sfm <- dat %>%
filter(Type_assay %in% c("Recognition", "Habituation", "Conditioning"),
Type_reinforcement %in% c("Appetitive", "Aversive", "Not applicable"),
Type_stress_exposure %in% c("Restraint", "Noise", "MS", "Combination"),
Stress_duration %in% c("Chronic", "Acute"))
VCV_Sfm <- impute_covariance_matrix(vi = dat_Sfm$lnRRV_E, cluster = dat_Sfm$Study_ID, r = 0.5)
mod_Sfm <- rma.mv(yi = lnRR_Sa, V = VCV_Sfm, mod = ~ Type_assay -1 + Learning_vs_memory + Type_reinforcement + Type_stress_exposure + Age_stress_exposure + Stress_duration, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat_Sfm)
#summary(mod_Sfm)
#r2_ml(mod_Sfm)
res_Sfm <- dredge(mod_Sfm, trace=2)
saveRDS(res_Sfm, file = here("Rdata", "res_Sfm.rds"))
# also saving the full model and data
saveRDS(mod_Sfm, file = here("Rdata", "mod_Sfm.rds"))
saveRDS(dat_Sfm, file = here("Rdata", "dat_Sfm.rds"))dat_Sfm <- readRDS(file = here("Rdata", "dat_Sfm.rds"))
mod_Sfm <- readRDS(file = here("Rdata", "mod_Sfm.rds"))
res_Sfm <- readRDS(file = here("Rdata", "res_Sfm.rds"))
res_Sfm2<- subset(res_Sfm, delta <= 6, recalc.weights=FALSE)
importance(res_Sfm2) ## Type_assay Stress_duration Type_stress_exposure
## Sum of weights: 0.91 0.88 0.22
## N containing models: 7 6 2
## Learning_vs_memory Age_stress_exposure Type_reinforcement
## Sum of weights: 0.16 0.04 0.04
## N containing models: 2 1 1Publication bias & sensitivity analysis
Publication bias
# funnel plot
Funnel_S <- funnel(mod_Sfm, xlab = "lnRR", ylab = "Standard Error")
Funnel_S## x y slab
## 1 -0.113053232 0.36468860 1
## 2 -0.015439898 0.20610340 2
## 3 -0.248010857 0.31117978 3
## 4 -0.232933972 0.39558646 4
## 5 -0.042387513 0.28415283 5
## 6 -0.371377055 0.24616892 6
## 7 -0.299243891 0.33372726 7
## 8 0.044834345 0.28792375 8
## 9 0.126615612 0.17056933 9
## 10 -0.177388895 0.16829598 10
## 11 -0.247450735 0.25159343 11
## 12 0.460979011 0.26162092 12
## 13 -0.554560458 0.33539070 13
## 14 0.680693862 0.30867691 14
## 15 -0.471382295 0.25172620 15
## 16 -0.394493983 0.23638600 16
## 17 -0.650018042 0.27849269 17
## 18 -0.300841465 0.25362984 18
## 19 0.027931562 0.19059106 19
## 20 0.080720040 0.19033477 20
## 21 0.041790076 0.24429542 21
## 22 0.203537585 0.10208024 22
## 23 0.124260874 0.14463442 23
## 24 0.116691372 0.14706024 24
## 25 -0.121449889 0.14833999 25
## 26 -0.001971635 0.19205933 26
## 27 -0.003907599 0.20022315 27
## 28 0.120523677 0.21113795 28
## 29 0.404659861 0.21388659 29
## 30 -0.109516461 0.22302661 30
## 31 -0.001061616 0.19288079 31
## 32 -0.004468007 0.19262936 32
## 33 0.043909789 0.18566567 33
## 34 0.115869661 0.18965184 34
## 35 -0.011278194 0.09467548 35
## 36 0.175061863 0.18048966 36
## 37 -0.122130273 0.22610402 37
## 38 0.002684314 0.17636045 38
## 39 0.035121843 0.18537686 39
## 40 -0.156416652 0.15860530 40
## 41 -0.138825592 0.15899389 41
## 42 0.053065175 0.17943678 42
## 43 0.138424978 0.18297543 43
## 44 0.102483572 0.18367269 44
## 45 0.170732200 0.19143563 45
## 46 0.139838423 0.18382247 46
## 47 0.128377093 0.17993531 47
## 48 0.221166287 0.18370137 48
## 49 0.044994292 0.18507026 49
## 50 -0.255771493 0.15305831 50
## 51 0.040084756 0.13800224 51
## 52 0.117345431 0.17133452 52
## 53 0.225493174 0.17017071 53
## 54 -0.041531740 0.18021370 54
## 55 0.380896989 0.79456429 55
## 56 0.014647193 0.18626025 56
## 57 -0.239220751 0.20755664 57
## 58 0.200890800 0.26208602 58
## 59 0.478075273 0.39791794 59
## 60 -0.693031272 0.44427913 60
## 61 -0.446007512 0.22443320 61
## 62 -0.385573815 0.31627073 62
## 63 0.187438535 0.29281093 63
## 64 -0.357529861 0.30767463 64
## 65 -0.206432699 0.18443973 65
## 66 -0.072514197 0.18259794 66
## 67 -0.058864366 0.39854817 67
## 68 0.552319003 0.28778615 68
## 69 0.499178177 0.26916891 69
## 70 0.527506647 0.32337519 70
## 71 -0.162795600 0.23927168 71
## 72 -0.032389680 0.19746325 72
## 73 0.107665252 0.24604039 73
## 74 0.209204201 0.16352434 74
## 75 -0.174302918 0.19626869 75
## 76 -0.021661369 0.19202699 76
## 77 -0.024341578 0.23180570 77
## 78 0.052275983 0.18465565 78
## 79 0.331124135 0.46142545 79
## 80 -0.102398694 0.20899450 80
## 81 0.349947062 0.56232826 81
## 82 0.203444436 0.16285272 82#calculating inv effective sample size for use in full meta-regression
dat_Sfm$sqrt_inv_e_n <- with(dat_Sfm, sqrt(1/CC_n + 1/EC_n + 1/ES_n + 1/CS_n))
#time lag bias and eggers regression
dat_Sfm$c_Year_published <- as.vector(scale(dat_Sfm$Year_published, scale = F))
PB_MR_S<- rma.mv(lnRR_Sa, lnRRV_S, mods = ~1 + sqrt_inv_e_n + c_Year_published + Type_assay +Learning_vs_memory + Type_reinforcement + Type_stress_exposure + Age_stress_exposure, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
method = "REML",
test = "t",
data = dat_Sfm,
control=list(optimizer="optim", optmethod="Nelder-Mead"))
estimates_PB_MR_S<- estimates.CI(PB_MR_S)
estimates_PB_MR_S## estimate mean lower upper
## 1 intrcpt -0.0531565147 -0.80198435 0.69567132
## 2 sqrt_inv_e_n 0.2644127720 -0.90453637 1.43336191
## 3 c_Year_published -0.0004505009 -0.02602183 0.02512083
## 4 Type_assayHabituation -0.6533807423 -1.04044969 -0.26631179
## 5 Type_assayRecognition -0.1444088134 -0.45497533 0.16615770
## 6 Learning_vs_memoryMemory -0.0795090078 -0.18171343 0.02269541
## 7 Type_reinforcementAversive 0.0994462558 -0.12019678 0.31908929
## 8 Type_reinforcementNot applicable 0.2639213412 -0.10091990 0.62876258
## 9 Type_stress_exposureMS -0.0627988969 -0.76070352 0.63510573
## 10 Type_stress_exposureNoise 0.0539297569 -0.80795528 0.91581480
## 11 Type_stress_exposureRestraint -0.1701899069 -0.56841160 0.22803179
## 12 Age_stress_exposureAdult -0.1949729974 -0.56490678 0.17496079
## 13 Age_stress_exposureEarly postnatal -0.1465963343 -0.89061343 0.59742076
## 14 Age_stress_exposurePrenatal -0.1798979551 -0.63426064 0.27446473#no effect of inv_effective sample size or year publishedLeave-one-out sensitivity analysis
dat$Study_ID <- as.factor(dat$Study_ID)
LeaveOneOut_effectsize <- list()
for(i in 1:length(levels(dat$Study_ID))){
LeaveOneOut_effectsize[[i]] <- rma.mv(yi = lnRR_Sa, V = lnRRV_S,
random = list(~1 | Study_ID,~1| ES_ID, ~1 | Strain),
method = "REML", data = dat[dat$Study_ID
!= levels(dat$Study_ID)[i], ])}
# writing function for extracting est, ci.lb, and ci.ub from all models
est.func <- function(mod_E0){
df <- data.frame(est = mod_E0$b, lower = mod_E0$ci.lb, upper = mod_E0$ci.ub)
return(df)
}
#using dplyr to form data frame
MA_CVR_S <- lapply(LeaveOneOut_effectsize, function(x) est.func(x))%>% bind_rows %>% mutate(left_out = levels(dat$Study_ID))
saveRDS(MA_CVR_S,file = here("Rdata", "MA_CVR_S.rds"))MA_CVR_S <- readRDS(file = here("Rdata", "MA_CVR_S.rds"))
#telling ggplot to stop reordering factors
MA_CVR_S$left_out<- as.factor(MA_CVR_S$left_out)
MA_CVR_S$left_out<-factor(MA_CVR_S$left_out, levels = MA_CVR_S$left_out)
#plotting
leaveoneout_S <- ggplot(MA_CVR_S) +
geom_hline(yintercept = 0, lty = 2, lwd = 1) +
geom_hline(yintercept = mod_S0$ci.lb, lty = 3, lwd = 0.75, colour = "black") +
geom_hline(yintercept = mod_S0$b, lty = 1, lwd = 0.75, colour = "black") +
geom_hline(yintercept = mod_S0$ci.ub, lty = 3, lwd = 0.75, colour = "black") +
geom_pointrange(aes(x = left_out, y = est, ymin = lower, ymax = upper)) +
xlab("Study left out") +
ylab("lnRR, 95% CI") +
coord_flip() +
theme(panel.grid.minor = element_blank())+
theme_bw() + theme(panel.grid.major = element_blank()) +
theme(panel.grid.minor.x = element_blank() ) +
theme(axis.text.y = element_text(size = 6))
leaveoneout_S
dat$Study_ID <- as.integer(dat$Study_ID)Interaction of stress and EE
Intercept
Enriched and stress animals are better at learning and memory.
mod_ES0 <- rma.mv(yi = lnRR_ESa, V = VCV_ES, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_ES0) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.8178 81.6355 89.6355 99.6790 90.1006
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0316 0.1777 30 no Study_ID
## sigma^2.2 0.0229 0.1513 92 no ES_ID
## sigma^2.3 0.0030 0.0544 6 no Strain
##
## Test for Heterogeneity:
## Q(df = 91) = 303.2179, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.1229 0.0596 2.0605 91 0.0422 0.0044 0.2414 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_ES0) ## I2_total I2_Study_ID I2_ES_ID I2_Strain
## 0.81703063 0.44913873 0.32576306 0.04212884orchard_plot(mod_ES0, mod = "Int", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Intercept with outlier removed
dat_outliers <- dat %>%
filter(ES_ID != 88)
VCV_ES_outliers <- impute_covariance_matrix(vi = dat_outliers$lnRRV_E, cluster = dat_outliers$Study_ID, r = 0.5)
mod_ESoutlier <- rma.mv(yi = lnRR_ESa, V = VCV_ES_outliers, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat_outliers)
summary(mod_ESoutlier)##
## Multivariate Meta-Analysis Model (k = 91; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.1745 80.3490 88.3490 98.3483 88.8196
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0351 0.1873 30 no Study_ID
## sigma^2.2 0.0703 0.2652 91 no ES_ID
## sigma^2.3 0.0218 0.1476 6 no Strain
##
## Test for Heterogeneity:
## Q(df = 90) = 1151.6019, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.2377 0.0990 2.4013 90 0.0184 0.0410 0.4343 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1orchard_plot(mod_ESoutlier, mod = "Int", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Meta-regression
Uni-moderator meta-regression
Type of assay
The type of learning/memory response
VCV_ES1 <- impute_covariance_matrix(vi = dat1$lnRRV_ES, cluster = dat$Study_ID, r = 0.5)
mod_ES1 <- rma.mv(yi = lnRR_ESa, V = VCV_ES1, mod = ~Type_assay-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat1)
summary(mod_ES1)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -39.0874 78.1748 90.1748 105.1066 91.1992
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0370 0.1924 30 no Study_ID
## sigma^2.2 0.0192 0.1386 92 no ES_ID
## sigma^2.3 0.0018 0.0422 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 89) = 293.9385, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 89) = 3.1062, p-val = 0.0305
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## Type_assayConditioning 0.1525 0.0589 2.5877 89 0.0113 0.0354 0.2696
## Type_assayHabituation 0.1990 0.1415 1.4070 89 0.1629 -0.0820 0.4801
## Type_assayRecognition -0.0048 0.0800 -0.0606 89 0.9518 -0.1637 0.1541
##
## Type_assayConditioning *
## Type_assayHabituation
## Type_assayRecognition
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES1) ## R2_marginal R2_coditional
## 0.05775809 0.97105550Learning_ES <- orchard_plot(mod_ES1, mod = "Type_assay", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 3, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
axis.text.x = element_text(size = 10), # change font sizes
axis.text.y = element_text(size = 10),
legend.title = element_text(size = 7),
legend.text = element_text(size = 7))
Learning_ES
Learning vs Memory
Is the assay broadly measuring learning or memory?
mod_ES2 <- rma.mv(yi = lnRR_ESa, V = VCV_ES, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_ES2) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.4769 80.9539 90.9539 103.4529 91.6682
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0292 0.1708 30 no Study_ID
## sigma^2.2 0.0232 0.1524 92 no ES_ID
## sigma^2.3 0.0034 0.0582 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 90) = 299.1854, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 90) = 2.9219, p-val = 0.0590
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Learning_vs_memoryLearning 0.1744 0.0722 2.4166 90 0.0177 0.0310
## Learning_vs_memoryMemory 0.1057 0.0619 1.7065 90 0.0914 -0.0174
## ci.ub
## Learning_vs_memoryLearning 0.3178 *
## Learning_vs_memoryMemory 0.2288 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES2) ## R2_marginal R2_coditional
## 0.0197648 0.9405080LvsM_ES <- orchard_plot(mod_ES2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 3, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
axis.text.x = element_text(size = 10), # change font sizes
axis.text.y = element_text(size = 10),
legend.title = element_text(size = 7),
legend.text = element_text(size = 7))
LvsM_ES 
Type of reinforcement
The type of conditioning used
VCV_ES2 <- impute_covariance_matrix(vi = dat2$lnRRV_ES, cluster = dat2$Study_ID, r = 0.5)
mod_ES3 <- rma.mv(yi = lnRR_ESa, V = VCV_ES2, mod = ~ Type_reinforcement-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat2)
summary(mod_ES3)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -39.0604 78.1208 90.1208 105.0526 91.1452
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0382 0.1954 30 no Study_ID
## sigma^2.2 0.0189 0.1377 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 89) = 293.4724, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 89) = 3.2547, p-val = 0.0254
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_reinforcementAppetitive 0.1007 0.1075 0.9366 89 0.3515 -0.1129
## Type_reinforcementAversive 0.1573 0.0569 2.7618 89 0.0070 0.0441
## Type_reinforcementNot applicable 0.0147 0.0702 0.2101 89 0.8341 -0.1247
## ci.ub
## Type_reinforcementAppetitive 0.3143
## Type_reinforcementAversive 0.2704 **
## Type_reinforcementNot applicable 0.1541
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES3) ## R2_marginal R2_coditional
## 0.0586952 1.0000000Reinforcement_ES <- orchard_plot(mod_ES3, mod = "Type_reinforcement", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 3, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
axis.text.x = element_text(size = 10), # change font sizes
axis.text.y = element_text(size = 10),
legend.title = element_text(size = 7),
legend.text = element_text(size = 7))
Reinforcement_ES 
Type of stress
The type of stress manipulation
VCV_ES3 <- impute_covariance_matrix(vi = dat3$lnRRV_ES, cluster = dat3$Study_ID, r = 0.5)
mod_ES4 <- rma.mv(yi = lnRR_ESa, V = VCV_ES3, mod = ~Type_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat3)
summary(mod_ES4)##
## Multivariate Meta-Analysis Model (k = 85; method: REML)
##
## logLik Deviance AIC BIC AICc
## -34.4046 68.8091 82.8091 99.5703 84.3434
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0426 0.2064 25 no Study_ID
## sigma^2.2 0.0232 0.1524 85 no ES_ID
## sigma^2.3 0.0104 0.1021 4 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 81) = 281.9708, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 81) = 0.5137, p-val = 0.7258
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_stress_exposureCombination 0.1111 0.1458 0.7618 81 0.4484 -0.1790
## Type_stress_exposureMS 0.1185 0.1099 1.0784 81 0.2840 -0.1001
## Type_stress_exposureNoise 0.1651 0.1795 0.9198 81 0.3604 -0.1920
## Type_stress_exposureRestraint 0.1374 0.1252 1.0978 81 0.2755 -0.1116
## ci.ub
## Type_stress_exposureCombination 0.4011
## Type_stress_exposureMS 0.3370
## Type_stress_exposureNoise 0.5221
## Type_stress_exposureRestraint 0.3865
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES4)## R2_marginal R2_coditional
## 0.004455703 0.863910284Stressor_ES <- orchard_plot(mod_ES4, mod = "Type_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 3, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
axis.text.x = element_text(size = 10), # change font sizes
axis.text.y = element_text(size = 10),
legend.title = element_text(size = 7),
legend.text = element_text(size = 7))
Stressor_ES 
Age of stress
The age at which the individuals were exposed to the stressor.
mod_ES5 <-rma.mv(yi = lnRR_ESa, V = VCV_ES, mod = ~Age_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_ES5) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -39.0917 78.1834 92.1834 109.5247 93.5834
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0295 0.1718 30 no Study_ID
## sigma^2.2 0.0232 0.1522 92 no ES_ID
## sigma^2.3 0.0091 0.0954 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 286.4225, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 1.5932, p-val = 0.1832
##
## Model Results:
##
## estimate se tval df pval
## Age_stress_exposureAdolescent -0.0137 0.1762 -0.0775 88 0.9384
## Age_stress_exposureAdult 0.1677 0.1140 1.4708 88 0.1449
## Age_stress_exposureEarly postnatal 0.1067 0.0920 1.1602 88 0.2491
## Age_stress_exposurePrenatal 0.3179 0.1311 2.4254 88 0.0173
## ci.lb ci.ub
## Age_stress_exposureAdolescent -0.3639 0.3366
## Age_stress_exposureAdult -0.0589 0.3942
## Age_stress_exposureEarly postnatal -0.0761 0.2896
## Age_stress_exposurePrenatal 0.0574 0.5784 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES5) ## R2_marginal R2_coditional
## 0.09276574 0.86630830Age_stress_ES<-orchard_plot(mod_ES5, mod = "Age_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 3, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
axis.text.x = element_text(size = 10), # change font sizes
axis.text.y = element_text(size = 10),
legend.title = element_text(size = 7),
legend.text = element_text(size = 7))
Age_stress_ES
Stress duration
How long was the stress applied for (chronic = every day for 7 days or more)? This has the highest marginal R2 (currentl nearly 43%) - need to redo without outlier
VCV_ES4 <- impute_covariance_matrix(vi = dat4$lnRRV_ES, cluster = dat4$Study_ID, r = 0.5)
mod_ES6 <-rma.mv(yi = lnRR_ESa, V = VCV_ES4, mod = ~Stress_duration-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat4)
summary(mod_ES6) ##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -35.9010 71.8020 81.8020 94.1315 82.5427
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0133 0.1155 29 no Study_ID
## sigma^2.2 0.0260 0.1611 89 no ES_ID
## sigma^2.3 0.0080 0.0895 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 87) = 278.4877, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 87) = 4.3877, p-val = 0.0153
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## Stress_durationAcute -0.0367 0.0930 -0.3946 87 0.6941 -0.2216 0.1482
## Stress_durationChronic 0.1854 0.0732 2.5347 87 0.0130 0.0400 0.3308
##
## Stress_durationAcute
## Stress_durationChronic *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES6) ## R2_marginal R2_coditional
## 0.1598311 0.8575918Duration_ES<- orchard_plot(mod_ES6, mod = "Stress_duration", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 3, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
axis.text.x = element_text(size = 10), # change font sizes
axis.text.y = element_text(size = 10),
legend.title = element_text(size = 7),
legend.text = element_text(size = 7))
Duration_ES
Social enrichment
Does EE also include a manipulation of social environment (i.e., number of individuals in EE relative to control)?
VCV_ES5 <- impute_covariance_matrix(vi = dat5$lnRRV_ES, cluster = dat5$Study_ID, r = 0.5)
mod_ES7<- rma.mv(yi = lnRR_ESa, V = VCV_ES5, mod = ~EE_social-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat5)
summary(mod_ES7)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.1871 80.3742 90.3742 102.8733 91.0885
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0311 0.1762 30 no Study_ID
## sigma^2.2 0.0230 0.1518 92 no ES_ID
## sigma^2.3 0.0046 0.0676 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 90) = 302.0860, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 90) = 2.4198, p-val = 0.0947
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## EE_socialNon-social 0.0750 0.0807 0.9300 90 0.3549 -0.0853 0.2353
## EE_socialSocial 0.1720 0.0788 2.1842 90 0.0315 0.0156 0.3285 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES7) ## R2_marginal R2_coditional
## 0.03751906 0.92501263Social_ES <- orchard_plot(mod_ES7, mod = "EE_social", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 3, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
axis.text.x = element_text(size = 10), # change font sizes
axis.text.y = element_text(size = 10),
legend.title = element_text(size = 7),
legend.text = element_text(size = 7))
Social_ES
Exercise enrichment
Does the form of enrichment include exercise through a running wheel or treadmill?
mod_ES8<- rma.mv(yi = lnRR_ESa, V = VCV_ES, mod = ~EE_exercise-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_ES8)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.4968 80.9935 90.9935 103.4926 91.7078
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0325 0.1803 30 no Study_ID
## sigma^2.2 0.0230 0.1517 92 no ES_ID
## sigma^2.3 0.0065 0.0805 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 90) = 297.3270, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 90) = 1.8401, p-val = 0.1647
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## EE_exerciseExercise 0.1051 0.0780 1.3474 90 0.1812 -0.0499 0.2602
## EE_exerciseNo exercise 0.1687 0.0967 1.7448 90 0.0844 -0.0234 0.3609
##
## EE_exerciseExercise
## EE_exerciseNo exercise .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES8) ## R2_marginal R2_coditional
## 0.01474459 0.89712469Exercise_ES <- orchard_plot(mod_ES8, mod = "EE_exercise", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 3, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
axis.text.x = element_text(size = 10), # change font sizes
axis.text.y = element_text(size = 10),
legend.title = element_text(size = 7),
legend.text = element_text(size = 7))
Exercise_ES
Order to treatment exposure
What order were animals exposed to stress and EE
mod_ES9 <- rma.mv(yi = lnRR_ESa, V = VCV_ES, mod = ~Exposure_order -1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_ES9)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -39.0408 78.0817 90.0817 105.0135 91.1061
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0316 0.1777 30 no Study_ID
## sigma^2.2 0.0227 0.1507 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 89) = 292.2561, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 89) = 3.1546, p-val = 0.0287
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Exposure_orderConcurrently 0.1492 0.1208 1.2351 89 0.2201 -0.0909
## Exposure_orderEnrichment first -0.1782 0.1659 -1.0744 89 0.2856 -0.5079
## Exposure_orderStress first 0.1370 0.0526 2.6046 89 0.0108 0.0325
## ci.ub
## Exposure_orderConcurrently 0.3893
## Exposure_orderEnrichment first 0.1514
## Exposure_orderStress first 0.2414 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES9)## R2_marginal R2_coditional
## 0.1027207 1.0000000Order_ES <- orchard_plot(mod_ES9, mod = "Exposure_order", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 3, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
axis.text.x = element_text(size = 10), # change font sizes
axis.text.y = element_text(size = 10),
legend.title = element_text(size = 7),
legend.text = element_text(size = 7))
Order_ES 
Age of enrichment
What age were individuals exposed to EE
VCV_ES6 <- impute_covariance_matrix(vi = dat6$lnRRV_ES, cluster = dat6$Study_ID, r = 0.5)
mod_ES10 <- rma.mv(yi = lnRR_ESa, V = VCV_ES6, mod = ~Age_EE_exposure-1, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat6)
summary(mod_ES10) ##
## Multivariate Meta-Analysis Model (k = 88; method: REML)
##
## logLik Deviance AIC BIC AICc
## -36.6407 73.2813 83.2813 95.5531 84.0313
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0332 0.1822 29 no Study_ID
## sigma^2.2 0.0207 0.1439 88 no ES_ID
## sigma^2.3 0.0007 0.0265 6 no Strain
##
## Test for Residual Heterogeneity:
## QE(df = 86) = 288.6493, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 86) = 3.1308, p-val = 0.0487
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Age_EE_exposureAdolescent 0.1284 0.0583 2.2006 86 0.0304 0.0124
## Age_EE_exposureAdult 0.1247 0.0921 1.3538 86 0.1793 -0.0584
## ci.ub
## Age_EE_exposureAdolescent 0.2444 *
## Age_EE_exposureAdult 0.3077
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES10) ## R2_marginal R2_coditional
## 0.0000400928 0.9871095822Age_enrichment_ES <- orchard_plot(mod_ES10, mod = "Age_EE_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 3, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
axis.text.x = element_text(size = 10), # change font sizes
axis.text.y = element_text(size = 10),
legend.title = element_text(size = 7),
legend.text = element_text(size = 7))
Age_enrichment_ES
Meta-regression: multi-moderator model
dat_ESfm <- dat %>%
filter(Type_assay %in% c("Recognition", "Habituation", "Conditioning"),
Type_reinforcement %in% c("Appetitive", "Aversive", "Not applicable"),
EE_social %in% c("Social", "Non-social"),
Age_EE_exposure %in% c("Adult", "Adolescent"),
Type_stress_exposure %in% c("Restraint", "Noise", "MS", "Combination"),
Stress_duration %in% c("Chronic", "Acute"),
Age_stress_exposure %in% c("Prenatal", "Early postnatal", "Adult"))
VCV_ESfm <- impute_covariance_matrix(vi = dat_ESfm$lnRRV_ES, cluster = dat_ESfm$Study_ID, r = 0.5)
mod_ESfm <- rma.mv(yi = lnRR_Sa, V = VCV_ESfm, mod = ~Type_assay-1 + Learning_vs_memory + Type_reinforcement + EE_social + EE_exercise + Age_EE_exposure + Type_stress_exposure + Age_stress_exposure + Stress_duration + Exposure_order, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat_ESfm)
#summary(mod_ESfm)
#r2_ml(mod_ESfm)
res_ESfm <- dredge(mod_ESfm, trace=2)
saveRDS(res_ESfm, file = here("Rdata", "res_ESfm.rds"))
# also saving the full model and data
saveRDS(mod_ESfm, file = here("Rdata", "mod_ESfm.rds"))
saveRDS(dat_ESfm, file = here("Rdata", "dat_ESfm.rds"))dat_ESfm <- readRDS(file = here("Rdata", "dat_ESfm.rds"))
mod_ESfm <- readRDS(file = here("Rdata", "mod_ESfm.rds"))
res_ESfm <- readRDS(file = here("Rdata", "res_ESfm.rds"))
res_ESfm2<- subset(res_ESfm, delta <= 6, recalc.weights=FALSE)
importance(res_ESfm2) ## Type_assay Stress_duration Age_EE_exposure EE_exercise
## Sum of weights: 0.65 0.63 0.26 0.11
## N containing models: 19 18 7 6
## Learning_vs_memory EE_social Age_stress_exposure
## Sum of weights: 0.08 0.08 0.05
## N containing models: 4 4 2
## Type_stress_exposure Exposure_order
## Sum of weights: 0.03 0.02
## N containing models: 2 1Publication bias & sensitivity analysis
Publication bias
Funnel_ES<-funnel(mod_ESfm, xlab = "lnRR", ylab = "Standard Error")
Funnel_ES## x y slab
## 1 -0.160575495 0.55343043 1
## 2 -0.274967420 0.70627539 2
## 3 -0.089909776 0.46527295 3
## 4 -0.418899318 0.36571674 4
## 5 -0.402782263 0.53501323 5
## 6 -0.058704027 0.45431531 6
## 7 0.098710844 0.15778981 7
## 8 -0.041805142 0.11742065 8
## 9 -0.234568128 0.44509952 9
## 10 0.473861619 0.42494140 10
## 11 -0.541677850 0.59839672 11
## 12 0.693576469 0.55379623 12
## 13 -0.506062429 0.32349206 13
## 14 -0.434662931 0.29560170 14
## 15 -0.752838102 0.41427250 15
## 16 -0.403661524 0.38640076 16
## 17 -0.074888497 0.14501213 17
## 18 -0.016611205 0.15220818 18
## 19 0.208016615 0.31484827 19
## 20 0.066418083 0.06641794 20
## 21 -0.016988105 0.13630983 21
## 22 0.102490150 0.27788492 22
## 23 0.077633012 0.12209543 23
## 24 0.265433711 0.18060215 24
## 25 0.549569896 0.20386929 25
## 26 0.029904759 0.23531678 26
## 27 0.036477985 0.10420416 27
## 28 0.033071595 0.10236053 28
## 29 0.154387233 0.13226659 29
## 30 0.068347398 0.10358055 30
## 31 -0.066651554 0.06766794 31
## 32 0.180810374 0.14954583 32
## 33 -0.116381761 0.30939379 33
## 34 0.008432825 0.12562534 34
## 35 0.040870354 0.17488050 35
## 36 0.039701546 0.09315773 36
## 37 0.119572535 0.09839159 37
## 38 0.083631129 0.10349806 38
## 39 0.151879757 0.14903289 39
## 40 0.120985979 0.10446433 40
## 41 0.031045847 0.10043162 41
## 42 0.118346228 0.11005125 42
## 43 0.155471736 0.12842298 43
## 44 -0.100968105 0.08874519 44
## 45 0.044022534 0.07229828 45
## 46 0.101114679 0.16689703 46
## 47 0.203773608 0.15471161 47
## 48 0.088563198 0.17334032 48
## 49 -0.005169248 0.17866865 49
## 50 0.434942304 0.36859082 50
## 51 0.554127069 0.73098123 51
## 52 -0.616979476 0.68762297 52
## 53 -0.364466901 0.25174124 53
## 54 -0.304033204 0.52455475 54
## 55 0.230466950 0.44507651 55
## 56 -0.314501446 0.47347958 56
## 57 -0.044976662 0.10592411 57
## 58 0.088941840 0.09196930 58
## 59 -0.100897815 0.73013998 59
## 60 0.510285555 0.59479999 60
## 61 0.451655914 0.34843255 61
## 62 0.479984384 0.70816567 62
## 63 -0.204829048 0.32322526 63
## 64 -0.079911943 0.14926781 64
## 65 0.065631803 0.33253908 65
## 66 0.186788247 0.09926520 66
## 67 -0.216336366 0.15728452 67
## 68 -0.069183632 0.12020637 68
## 69 -0.074226124 0.44173325 69
## 70 0.038912354 0.12603637 70
## 71 0.441601579 1.08275832 71
## 72 -0.149920957 0.21396887 72
## 73 0.302424799 1.01869334 73
## 74 0.205736931 0.09751054 74#year published was scaled previously under stress PB
dat_ESfm$sqrt_inv_e_n <- with(dat_ESfm, sqrt(1/CC_n + 1/EC_n + 1/ES_n + 1/CS_n))
PB_MR_ES<- rma.mv(lnRR_ESa, lnRRV_ES, mods = ~1 + sqrt_inv_e_n + Year_published + Learning_vs_memory + Type_assay + Type_reinforcement + EE_social + EE_exercise + Age_stress_exposure, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain), method = "REML", test = "t",
data = dat_ESfm)
estimates_PB_MR_ES<- estimates.CI(PB_MR_ES)
estimates_PB_MR_ES## estimate mean lower upper
## 1 intrcpt 29.134353111 -30.75347607 89.0221823
## 2 sqrt_inv_e_n -0.673866709 -1.71164812 0.3639147
## 3 Year_published -0.014252207 -0.04395022 0.0154458
## 4 Learning_vs_memoryMemory 0.012836924 -0.11463581 0.1403097
## 5 Type_assayHabituation 0.104424947 -0.43068420 0.6395341
## 6 Type_assayRecognition -0.030511298 -0.47521428 0.4141917
## 7 Type_reinforcementAversive 0.006308661 -0.37520415 0.3878215
## 8 Type_reinforcementNot applicable -0.188090210 -0.76103491 0.3848545
## 9 EE_socialSocial 0.153788955 -0.17496170 0.4825396
## 10 EE_exerciseNo exercise 0.101557466 -0.21974101 0.4228559
## 11 Age_stress_exposureEarly postnatal 0.104819490 -0.21762872 0.4272677
## 12 Age_stress_exposurePrenatal 0.127578525 -0.32580147 0.5809585#no effect of inv_effective sample size or year publishedLeave-one-out analysis
dat$Study_ID <- as.factor(dat$Study_ID)
LeaveOneOut_effectsize <- list()
for(i in 1:length(levels(dat$Study_ID))){
LeaveOneOut_effectsize[[i]] <- rma.mv(yi = lnRR_Ea, V = lnRRV_E,
random = list(~1 | Study_ID,~1| ES_ID, ~1 | Strain),
method = "REML", data = dat[dat$Study_ID
!= levels(dat$Study_ID)[i], ])}
# writing function for extracting est, ci.lb, and ci.ub from all models
est.func <- function(mod_E0){
df <- data.frame(est = mod_E0$b, lower = mod_E0$ci.lb, upper = mod_E0$ci.ub)
return(df)
}
#using dplyr to form data frame
MA_CVR_ES <- lapply(LeaveOneOut_effectsize, function(x) est.func(x))%>% bind_rows %>% mutate(left_out = levels(dat$Study_ID))
saveRDS(MA_CVR_ES, ,file = here("Rdata", "MA_CVR_ES.rds"))MA_CVR_ES<- readRDS(here("Rdata", "MA_CVR_ES.rds"))
#telling ggplot to stop reordering factors
MA_CVR_ES$left_out<- as.factor(MA_CVR_ES$left_out)
MA_CVR_ES$left_out<-factor(MA_CVR_ES$left_out, levels = MA_CVR_ES$left_out)
#plotting
leaveoneout_ES <- ggplot(MA_CVR_ES) +
geom_hline(yintercept = 0, lty = 2, lwd = 1) +
geom_hline(yintercept = mod_E0$ci.lb, lty = 3, lwd = 0.75, colour = "black") +
geom_hline(yintercept = mod_E0$b, lty = 1, lwd = 0.75, colour = "black") +
geom_hline(yintercept = mod_E0$ci.ub, lty = 3, lwd = 0.75, colour = "black") +
geom_pointrange(aes(x = left_out, y = est, ymin = lower, ymax = upper)) +
xlab("Study left out") +
ylab("lnRR, 95% CI") +
coord_flip() +
theme(panel.grid.minor = element_blank())+
theme_bw() + theme(panel.grid.major = element_blank()) +
theme(panel.grid.minor.x = element_blank() ) +
theme(axis.text.y = element_text(size = 6))
leaveoneout_ES
dat$Study_ID <- as.integer(dat$Study_ID)Combined orchard plot
mod_list1 <- list(mod_E0, mod_S0, mod_ES0)
mod_res1 <- lapply(mod_list1, function(x) mod_results(x, mod = "Int"))
merged1 <- submerge(mod_res1[[3]], mod_res1[[2]], mod_res1[[1]], mix = T)
merged1$mod_table$name <- factor(merged1$mod_table$name, levels = c("Intrcpt1",
"Intrcpt2", "Intrcpt3"),
labels = rev(c("Enrichment ME", "Stress ME", "Interaction")))
merged1$data$moderator <- factor(merged1$data$moderator, levels = c("Intrcpt1",
"Intrcpt2", "Intrcpt3"),
labels = rev(c("Enrichment ME", "Stress ME", "Interaction")))
orchard1<- orchard_plot(merged1, mod = "Int", xlab = "lnRR", angle = 0) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
xlim(-2,4.5) +
geom_point(aes(fill = name), size = 4, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling +
scale_colour_manual(values = c("#00AEEF","#00A651","#ED1C24"))+ # change colours
scale_fill_manual(values=c("#00AEEF","#00A651","#ED1C24"))+
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
orchard1
‘Pairwise’ effect sizes
Enrichment relative to control
VCV_E20 <- impute_covariance_matrix(vi = dat$lnRRV_E2, cluster = dat$Study_ID, r = 0.5)
#Model doesn't converge with VCV
mod_E20 <- rma.mv(yi = lnRR_E2a, V = VCV_E20, random = list(~1|Study_ID,
~ 1|Strain,
~1|ES_ID),
test = "t",
data = dat,
control=list(optimizer="optim", optmethod="Nelder-Mead"))
summary(mod_E20) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -7.3235 14.6470 22.6470 32.6904 23.1121
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0037 0.0611 30 no Study_ID
## sigma^2.2 0.0000 0.0000 6 no Strain
## sigma^2.3 0.0281 0.1675 92 no ES_ID
##
## Test for Heterogeneity:
## Q(df = 91) = 475.8327, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.1066 0.0291 3.6655 91 0.0004 0.0489 0.1644 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_E20) ## I2_total I2_Study_ID I2_Strain I2_ES_ID
## 8.610789e-01 1.011724e-01 2.607945e-09 7.599065e-01orchard_plot(mod_E20, mod = "Int", xlab = "lnRR")
Stress relative to control
VCV_S20 <- impute_covariance_matrix(vi = dat$lnRRV_S2, cluster = dat$Study_ID, r = 0.5)
mod_S20 <- rma.mv(yi = lnRR_S2a, V = VCV_S20, random = list(~1|Study_ID,
~ 1|Strain,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_S20) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -52.3561 104.7122 112.7122 122.7557 113.1773
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0323 0.1797 30 no Study_ID
## sigma^2.2 0.0000 0.0000 6 no Strain
## sigma^2.3 0.0798 0.2824 92 no ES_ID
##
## Test for Heterogeneity:
## Q(df = 91) = 1003.0694, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.1846 0.0522 -3.5360 91 0.0006 -0.2882 -0.0809 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_S20) ## I2_total I2_Study_ID I2_Strain I2_ES_ID
## 9.489604e-01 2.734220e-01 9.879730e-10 6.755384e-01orchard_plot(mod_S20, mod = "Int", xlab = "lnRR")
Enrichment + stress relative to control
VCV_ES20 <- impute_covariance_matrix(vi = dat$lnRRV_ES2, cluster = dat$Study_ID, r = 0.5)
mod_ES20 <- rma.mv(yi = lnRR_ES2a, V = VCV_ES20, random = list(~1|Study_ID,
~ 1|Strain,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES20) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -10.3625 20.7250 28.7250 38.7684 29.1901
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0039 0.0625 30 no Study_ID
## sigma^2.2 0.0014 0.0377 6 no Strain
## sigma^2.3 0.0227 0.1508 92 no ES_ID
##
## Test for Heterogeneity:
## Q(df = 91) = 402.2656, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.0801 0.0389 2.0594 91 0.0423 0.0028 0.1573 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_ES20) ## I2_total I2_Study_ID I2_Strain I2_ES_ID
## 0.81513970 0.11355557 0.04132363 0.66026050orchard_plot(mod_ES20, mod = "Int", xlab = "lnRR")
Enrichment + stress relative to stress
VCV_E30 <- impute_covariance_matrix(vi = dat$lnRRV_E3, cluster = dat$Study_ID, r = 0.5)
mod_E30 <- rma.mv(yi = lnRR_E3a, V = VCV_E30, random = list(~1|Study_ID,
~ 1|Strain,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E30) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -46.3447 92.6895 100.6895 110.7329 101.1546
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0235 0.1532 30 no Study_ID
## sigma^2.2 0.0263 0.1623 6 no Strain
## sigma^2.3 0.0543 0.2331 92 no ES_ID
##
## Test for Heterogeneity:
## Q(df = 91) = 790.0249, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.2465 0.1011 2.4389 91 0.0167 0.0457 0.4472 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_E30) ## I2_total I2_Study_ID I2_Strain I2_ES_ID
## 0.9456920 0.2132063 0.2391809 0.4933048orchard_plot(mod_E30, mod = "Int", xlab = "lnRR")
Enrichment + stress relative to enrichment
VCV_S30 <- impute_covariance_matrix(vi = dat$lnRRV_S3, cluster = dat$Study_ID, r = 0.5)
mod_S30 <- rma.mv(yi = lnRR_S3a, V = VCV_S30, random = list(~1|Study_ID,
~ 1|Strain,
~1|ES_ID),
test = "t",
data = dat,
control=list(optimizer="optim", optmethod="Nelder-Mead"))
summary(mod_S30) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -6.8788 13.7576 21.7576 31.8011 22.2228
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0000 0.0000 30 no Study_ID
## sigma^2.2 0.0009 0.0292 6 no Strain
## sigma^2.3 0.0276 0.1662 92 no ES_ID
##
## Test for Heterogeneity:
## Q(df = 91) = 540.3522, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.0087 0.0342 -0.2552 91 0.7992 -0.0766 0.0592
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_S30) ## I2_total I2_Study_ID I2_Strain I2_ES_ID
## 8.415428e-01 5.192043e-09 2.515933e-02 8.163835e-01orchard_plot(mod_S30, mod = "Int", xlab = "lnRR")
Combined orchard plot
mod_list2 <- list(mod_S30, mod_E30, mod_ES20, mod_S20, mod_E20) #rearranged the order so that it matches intext results
mod_res2 <- lapply(mod_list2, function(x) mod_results(x, mod = "Int"))
merged2 <- submerge(mod_res2[[1]], mod_res2[[2]], mod_res2[[3]], mod_res2[[4]], mod_res2[[5]], mix = T)
merged2$mod_table$name <- factor(merged2$mod_table$name, levels = c("Intrcpt1",
"Intrcpt2", "Intrcpt3", "Intrcpt4", "Intrcpt5"),
labels = rev(c("EC/CC", "CS/CC", "ES/CC", "ES/CS", "ES/EC")))
merged2$data$moderator <- factor(merged2$data$moderator, levels = c("Intrcpt1",
"Intrcpt2", "Intrcpt3", "Intrcpt4", "Intrcpt5"),
labels = rev(c("EC/CC", "CS/CC", "ES/CC", "ES/CS", "ES/EC")))
orchard2 <- orchard_plot(merged2, mod = "Int", xlab = "lnRR", angle = 0) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
xlim(-2,4.5) +
geom_point(aes(fill = name), size = 4, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
scale_colour_manual(values = c("#7B81BE","#D7DF23","#F37158","#75CBF2","#97D2B4"))+ # change colours
scale_fill_manual(values=c("#7B81BE","#D7DF23","#F37158","#75CBF2","#97D2B4"))+
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 15), # change font sizes
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
orchard2
Panel of ‘focal’ ES and ‘pairwise’ ES orchard plots
p1 <- orchard1 + orchard2 + plot_annotation(tag_levels = 'A')
p1
#saved as PDF: 6 x 15 inchesPanel of meta-regressions
Environmental enrichment
#Enrichment
E_mod <- (LvsM_E + Learning_E + Reinforcement_E)/ (Age_E + Exercise_E + Social_E) + plot_annotation(tag_levels = 'A')
E_mod
#saved as pdf 10 x 15 inchesStress
S_mod <- (LvsM_S + Learning_S + Reinforcement_S) / (Age_S + Stressor + Duration_S) + plot_annotation(tag_levels = 'A')
S_mod
#saved as pdf 10 x 15 inchesInteraction
ES_mod <- plot_grid(LvsM_ES, Learning_ES, Reinforcement_ES, Age_enrichment_ES, Age_stress_ES, Order_ES, Exercise_ES, Social_ES, Stressor_ES, Duration_ES,
labels = "AUTO", ncol = 5)
ES_mod
#saved as 10 x 20 inchesPanel of funnel plots
## quartz_off_screen
## 2Modelling with SMD
Environmental Enrichment
Intercept model
# TODO
#Why does the funnel plot look so strange and why is I2 higher?
mod_E0a <- rma.mv(yi = SMD_Ea, V = VCV_Ea, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_E0a)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -112.9351 225.8701 233.8701 243.9136 234.3353
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0000 0.0000 30 no Study_ID
## sigma^2.2 0.0000 0.0000 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Heterogeneity:
## Q(df = 91) = 17.8254, p-val = 1.0000
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.0249 0.0995 -0.2504 91 0.8028 -0.2226 0.1728
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_E0a)## I2_total I2_Study_ID I2_ES_ID I2_Strain
## 8.598505e-10 8.994874e-14 0.000000e+00 8.597605e-10funnel(mod_E0a, yaxis="seinv")
# VCV matrix
VCV_Efm <- impute_covariance_matrix(vi = dat_Efm$lnRRV_E, cluster = dat_Efm$Study_ID, r = 0.5)
mod_E0b <- rma.mv(yi = SMD_Ea, V = VCV_Efm, mod = ~Type_assay-1 + Learning_vs_memory-1 + Type_reinforcement-1 + EE_social-1 + EE_exercise-1 + Age_EE_exposure-1, random = list(~1|Study_ID,~1|ES_ID,~1|Strain),
test = "t",
data = dat_Efm)
funnel(mod_E0b, yaxis="seinv")
Stress
Intercept model
mod_S0a <- rma.mv(yi = SMD_Sa, V = VCV_Sa, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_S0a) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -134.4163 268.8326 276.8326 286.8760 277.2977
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0000 0.0001 30 no Study_ID
## sigma^2.2 0.0809 0.2845 92 no ES_ID
## sigma^2.3 0.0000 0.0000 6 no Strain
##
## Test for Heterogeneity:
## Q(df = 91) = 64.9393, p-val = 0.9823
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.0959 0.1193 -0.8040 91 0.4235 -0.3328 0.1410
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_S0a) ## I2_total I2_Study_ID I2_ES_ID I2_Strain
## 9.843664e-02 6.086831e-09 9.843664e-02 4.598665e-12funnel(mod_S0a, yaxis="seinv")
# VCV matrix
VCV_Sfm <- impute_covariance_matrix(vi = dat_Sfm$lnRRV_E, cluster = dat_Sfm$Study_ID, r = 0.5)
#assessing funnel on model model
mod_S0b <- rma.mv(yi = SMD_Sa, V = VCV_Sfm, mod = ~ Type_assay -1 + Learning_vs_memory + Type_reinforcement + Type_stress_exposure + Age_stress_exposure + Stress_duration, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat_Sfm)
funnel(mod_S0b, yaxis="seinv")
Interaction
Intercept model
mod_ES0a <- rma.mv(yi = SMD_ESa, V = VCV_ESa, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat)
summary(mod_ES0a)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -127.2849 254.5699 262.5699 272.6133 263.0350
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5212 0.7219 30 no Study_ID
## sigma^2.2 0.3989 0.6316 92 no ES_ID
## sigma^2.3 0.0000 0.0002 6 no Strain
##
## Test for Heterogeneity:
## Q(df = 91) = 283.7373, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.7126 0.1825 3.9042 91 0.0002 0.3500 1.0751 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_ES0a) ## I2_total I2_Study_ID I2_ES_ID I2_Strain
## 6.899076e-01 3.908063e-01 2.991013e-01 1.910481e-08funnel(mod_ES0a, yaxis="seinv")
# VCV matrix
VCV_ESfm <- impute_covariance_matrix(vi = dat_ESfm$lnRRV_E, cluster = dat_ESfm$Study_ID, r = 0.5)
mod_ES0b <- rma.mv(yi = SMD_Sa, V = VCV_ESfm, mod = ~Type_assay-1 + Learning_vs_memory + Type_reinforcement + EE_social + EE_exercise + Age_EE_exposure + Type_stress_exposure + Age_stress_exposure + Stress_duration + Exposure_order, random = list(~1|Study_ID,
~1|ES_ID,
~1|Strain),
test = "t",
data = dat_ESfm)
funnel(mod_ES0b, yaxis="seinv")
---
title: "Stress x EE MA"
authors: "Erin L Macartney, Malgorzata Lagisz, Shinichi Nakagawa"
subtitle: Supplementary Material
output: 
    
    rmdformats::readthedown:
      code_folding: hide
      code_download: true
editor_options: 
  chunk_output_type: console
  # output:
#   html_document:
#     code_folding: hide
#     df_print: kable
#     toc: TRUE
#     toc_float: TRUE
#     toc_depth: 4
#     collapsed: FALSE
#     YAML: rmdformats::robobook
---

```{r, include = FALSE}
knitr::opts_chunk$set(
message = FALSE,
warning = FALSE,
cache = TRUE, 
tidy = TRUE, 
echo = TRUE
)

rm(list = ls())
```

# Setting-up

## Loading packages

```{r}
pacman::p_load(tidyverse, 
               here,
               metafor,
               clubSandwich,
               orchaRd, 
               MuMIn, 
               patchwork,
               GoodmanKruskal,
               networkD3,
               ggplot2,
               visdat,
               ggalluvial,
               ggthemr,
               cowplot)
# needed for model selection using MuMIn within metafor
eval(metafor:::.MuMIn)

```

## Loading data and functions

```{r}
dat <- read_csv(here("Data","Data_raw.csv"))
# Load custom function to extract data 
source(here("R/Functions.R")) 
```

# Data exploration

## General  
``` {r, message = FALSE, warning = FALSE, eval = FALSE}
#Number of effect sizes
length(unique(dat$ES_ID))  

#Number of studies
length(unique(dat$Study_ID))

#Publication years
min(dat$Year_published) 
max(dat$Year_published)
```

## Explore associations among predictor variables   

```{r, results = 'hide'}
plot_missing <- vis_miss(dat) +
  theme(plot.title = element_text(hjust = 0.5, vjust = 3), 
        plot.margin = margin(t = 0.5, r = 3, b = 1, l = 1, unit = "cm")) +
  ggtitle("Missing data in the selected predictors") #no missing values

plot_missing
#useGoodman and Kruskal’s τ measure of association between categorical predictor variables (function from package GoodmanKruskal: https://cran.r-project.org/web/packages/GoodmanKruskal/vignettes/GoodmanKruskal.html)
#GKmatrix <- GKtauDataframe(subset(dat, select = c("Sex", "Type_assay", "Learning_vs_memory", #"Type_reinforcement",  "Type_stress_exposure", "Age_stress_exposure", "Stress_duration", #"EE_social_HR", "EE_exercise", "Age_EE_exposure", "Exposure_order", "Age_assay")))
#plot(GKmatrix)

#simple pairwise contingency tables
# table(dat$Type_assay, dat$Type_reinforcement) 
# table(dat$Age_stress_exposure, dat$Age_EE_exposure) 
# table(dat$Type_stress_exposure, dat$Age_stress_exposure)
# table(dat$Type_stress_exposure, dat$Age_assay)
# table(dat$Type_stress_exposure, dat$Stress_duration)
```

## Alluvial diagrams 

```{r redo and assemble plots , results = 'hide'}

#A. subjects info: species-strain-sex

freq_A <- as.data.frame(table(dat$Sex, dat$Common_species, dat$Strain)) %>% rename(Sex = Var1, Species = Var2, Strain = Var3) #make a data frame of frequencies for three selected variables
is_alluvia_form(as.data.frame(freq_A), axes = 1:3, silent = TRUE)

p1 <- ggplot(data = freq_A,
  aes(axis1 = Sex, axis2 = Species, axis3 = Strain, y = Freq)) +
  geom_alluvium(aes(fill = Sex)) +
  geom_flow() +
  geom_stratum(aes(fill = Sex)) +
  geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
  #theme_minimal() +
  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0, vjust = 3),
        axis.title.x = element_text(),
        axis.text.x = element_text(face="bold"),
        plot.margin = unit(c(1, 1, 0, 1), "cm")) +
  scale_x_discrete(limits = c("Sex", "Species", "Strain"), expand = c(0.15, 0.05), position = "top") +
  ggtitle("A  study subjects")

p1

#B. EE info: type-exercise-social EE

freq_B <- as.data.frame(table(dat$Type_EE_exposure, dat$EE_exercise, dat$EE_social)) %>% rename(Type_EE = Var1, EE_exercise = Var2, EE_social = Var3) #make a data frame of frequencies for three selected variables
is_alluvia_form(as.data.frame(freq_B), axes = 1:3, silent = TRUE)

p2 <- ggplot(data = freq_B,
  aes(axis1 = Type_EE, axis2 = EE_exercise, axis3 = EE_social, y = Freq)) +
  geom_alluvium(aes(fill = Type_EE)) +
  geom_flow() +
  geom_stratum(aes(fill = Type_EE)) +
  geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
  #theme_minimal() +
  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0, vjust = 3),
        axis.title.x = element_text(),
        axis.text.x = element_text(face="bold"),
        plot.margin = unit(c(1, 1, 0, 1), "cm")) +
  scale_x_discrete(limits = c("Type", "Exercise", "Social"), expand = c(0.1, 0.1), position = "top") +
  ggtitle("C  environmental enrichment")

p2

#C. stress info: age-duration-type stress

freq_C <- as.data.frame(table(dat$Age_stress_exposure, dat$Stress_duration, dat$Type_stress_exposure)) %>% rename(Age_stress = Var1, Duration_stress = Var2, Type_stress = Var3) #make a data frame of frequencies for three selected variables
is_alluvia_form(as.data.frame(freq_C), axes = 1:3, silent = TRUE)

p3 <- ggplot(data = freq_C,
  aes(axis1 = Age_stress, axis2 = Duration_stress, axis3 = Type_stress, y = Freq)) +
  geom_alluvium(aes(fill = Age_stress)) +
  geom_flow() +
  geom_stratum(aes(fill = Age_stress)) +
  geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
  #theme_minimal() +
  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0, vjust = 3),
        axis.title.x = element_text(),
        axis.text.x = element_text(face="bold"),
        plot.margin = unit(c(1, 1, 0, 1), "cm")) +
  scale_x_discrete(limits = c("Age", "Duration", "Type"), expand = c(0.1, 0.1), position = "top") +
  ggtitle("B  stress exposure")

p3

#D. assay info: L/M-type-reinforcement

freq_D <- as.data.frame(table(dat$Learning_vs_memory, dat$Type_assay, dat$Type_reinforcement)) %>% rename(Learning_Memory = Var1, Type = Var2, Reinforcement = Var3) #make a data frame of frequencies for three selected variables
is_alluvia_form(as.data.frame(freq_D), axes = 1:3, silent = TRUE)

p4 <- ggplot(data = freq_D,
  aes(axis1 = Learning_Memory, axis2 = Type, axis3 = Reinforcement, y = Freq)) +
  geom_alluvium(aes(fill = Learning_Memory)) +
  geom_flow() +
  geom_stratum(aes(fill = Learning_Memory)) +
  geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
  #theme_minimal() +
  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0, vjust = 3),
        axis.title.x = element_text(),
        axis.text.x = element_text(face="bold"),
        plot.margin = unit(c(1, 1, 0, 1), "cm")) +
  scale_x_discrete(limits = c("Learning_Memory", "Type", "Reinforcement"), expand = c(0.1, 0.1), position = "top") +
  ggtitle("D  cognitive assay")

p4

#p1 + scale_fill_brewer(palette = "Set3") #Pastel1

(p1 + scale_fill_brewer(palette = "Set3")) / (p2 + scale_fill_brewer(palette = "Set3")) / (p3 + scale_fill_brewer(palette = "Set3")) / (p4 + scale_fill_brewer(palette = "Set3")) + plot_layout(ncol = 1, heights = c(1,1,1,1,1))

#ggsave(file = "./figs/Alluvial_diagrams_v0.pdf", width = 10, height = 12, units = "cm", dpi = 300, scale = 2, device = cairo_pdf)
```

# Data organisation
Removing study with negative values, getting effect sizes from function, 'flipping' effect sizes so that all effect sizes are higher values = individuals do better and learning/memory, shifting negative values to positive as lnRR cannot use negative values, assigining human readable terms, and creating VCV of variance 

```{r}
#this code is to process the data using "Data_raw". Processed data after this step was loaded above
#removing study with negative values as these are unable to be used for lnRR
dat <- droplevels(dat[!dat$First_author == 'Wang',])

# #Getting effect sizes
# effect_size <- effect_set(CC_n = "CC_n", CC_mean = "CC_mean", CC_SD = "CC_SD",
#                           EC_n = "EC_n", EC_mean = "EC_mean" , EC_SD ="EC_SD",
#                           CS_n = "CS_n", CS_mean = "CS_mean", CS_SD = "CS_SD",
#                           ES_n = "ES_n", ES_mean = "ES_mean", ES_SD = "ES_SD",
#                           data = dat)
# #'pure' effect sizes
# effect_size2 <- effect_set2(CC_n = "CC_n", CC_mean = "CC_mean", CC_SD = "CC_SD",
#                           EC_n = "EC_n", EC_mean = "EC_mean" , EC_SD ="EC_SD",
#                           CS_n = "CS_n", CS_mean = "CS_mean", CS_SD = "CS_SD",
#                           ES_n = "ES_n", ES_mean = "ES_mean", ES_SD = "ES_SD",
#                           data = dat) 
#rounding down integer sample sizes 
dat$CC_n <- floor(dat$CC_n)
dat$EC_n <- floor(dat$EC_n)
dat$CS_n <- floor(dat$CS_n)
dat$ES_n <- floor(dat$CS_n)

# 'Focal' effect_size 
effect_size <- with(dat, mapply(effect_set, 
                      CC_n ,
                      CC_mean, 
                      CC_SD,
                      EC_n, 
                      EC_mean, 
                      EC_SD,
                      CS_n, 
                      CS_mean, 
                      CS_SD,
                      ES_n, 
                      ES_mean, 
                      ES_SD,
                      percent = Response_percent,
                      SIMPLIFY = FALSE))
effect_size <- map_dfr(effect_size, I)

# 'Pairwise' effect size

 effect_size2 <- with(dat, mapply(effect_set2, 
                      CC_n ,
                      CC_mean, 
                      CC_SD,
                      EC_n, 
                      EC_mean, 
                      EC_SD,
                      CS_n, 
                      CS_mean, 
                      CS_SD,
                      ES_n, 
                      ES_mean, 
                      ES_SD,
                      percent = Response_percent,
                      SIMPLIFY = FALSE))
effect_size2 <- map_dfr(effect_size2, I)

#Removing missing effect sizes
dim(dat)
full_info <- which(complete.cases(effect_size) == TRUE)

# adding effect sizes as column
dat <- bind_cols(dat, effect_size, effect_size2)
dat <- dat[full_info, ]

#Flipping 'lower is better' effect sizes
#flipping lnRR for values where higher = worse
dat$lnRR_Ea <- ifelse(dat$Response_direction == 2, dat$lnRR_E*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_E))
# currently NAswhich causes error
dat$lnRR_Sa  <- ifelse(dat$Response_direction == 2, dat$lnRR_S*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_S)) # currently NAswhich causes error
dat$lnRR_ESa <-  ifelse(dat$Response_direction == 2, dat$lnRR_ES*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_ES)) # currently NAswhich causes error
#flipping 'pure effect sizes'
dat$lnRR_E2a <- ifelse(dat$Response_direction == 2, dat$lnRR_E2*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_E2)) # currently NAswhich causes error
dat$lnRR_S2a  <- ifelse(dat$Response_direction == 2, dat$lnRR_S2*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_S2)) # currently NAswhich causes error
dat$lnRR_ES2a <-  ifelse(dat$Response_direction == 2, dat$lnRR_ES2*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_ES2)) # currently NAswhich causes error
dat$lnRR_E3a <-  ifelse(dat$Response_direction == 2, dat$lnRR_E3*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_E3)) # currently NAswhich causes error
dat$lnRR_S3a <-  ifelse(dat$Response_direction == 2, dat$lnRR_S3*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_S3)) # currently NAswhich causes error

#flipping SMD
dat$SMD_Ea <- ifelse(dat$Response_direction == 2, dat$SMD_E*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_E)) # currently NAswhich causes error
dat$SMD_Sa  <- ifelse(dat$Response_direction == 2, dat$SMD_S*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_S)) # currently NAswhich causes error
dat$SMD_ESa <-  ifelse(dat$Response_direction == 2, dat$SMD_ES*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_ES))

#assigning human readable terms
dat <- dat %>% mutate(Type_assay = case_when(Type_assay == 1 ~ "Habituation",
                                                Type_assay == 2 ~ "Conditioning",
                                                Type_assay == 3 ~ "Recognition", 
                                                Type_assay == 4 ~ "Unclear"),
                      Learning_vs_memory = case_when(Learning_vs_memory == 1 ~ "Learning",
                                                     Learning_vs_memory == 2 ~ "Memory", 
                                                     Learning_vs_memory == 3 ~ "Habituation"),
                      Type_reinforcement = case_when(Type_reinforcement== 1 ~"Appetitive",
                                                         Type_reinforcement== 2 ~ "Aversive",
                                                         Type_reinforcement== 3 ~ "Not applicable",
                                                         Type_reinforcement== 4 ~ "Unclear"),
                      Type_stress_exposure = case_when(Type_stress_exposure == 1 ~ "Density",
                                                       Type_stress_exposure == 2 ~ "Scent",
                                                       Type_stress_exposure == 3 ~ "Shock",
                                                       Type_stress_exposure == 4 ~ "Exertion",
                                                       Type_stress_exposure == 5 ~ "Restraint",
                                                       Type_stress_exposure == 6 ~ "MS",
                                                       Type_stress_exposure == 7 ~ "Circadian rhythm",
                                                       Type_stress_exposure == 8 ~ "Noise",
                                                       Type_stress_exposure == 9 ~ "Other",
                                                       Type_stress_exposure == 10 ~ "Combination",
                                                       Type_stress_exposure == 11 ~ "unclear"), 
                      Age_stress_exposure = case_when(Age_stress_exposure == 1 ~ "Prenatal",
                                                      Age_stress_exposure == 2 ~ "Early postnatal",
                                                      Age_stress_exposure == 3 ~ "Adolescent",
                                                      Age_stress_exposure == 4 ~ "Adult",
                                                      Age_stress_exposure == 5 ~ "Unclear"),
                      Stress_duration = case_when(Stress_duration == 1 ~ "Acute",
                                                  Stress_duration == 2 ~ "Chronic",
                                                  Stress_duration == 3 ~ "Intermittent",
                                                  Stress_duration == 4 ~ "Unclear"),
                      EE_social = case_when(EE_social == 1 ~ "Social",
                                            EE_social== 2 ~ "Non-social", 
                                            EE_social == 3 ~ "Unclear"), 
                      EE_exercise = case_when(EE_exercise == 1 ~ "Exercise", 
                                              EE_exercise == 2 ~ "No exercise"),
                      Age_EE_exposure = case_when(Age_EE_exposure == 1 ~ "Prenatal", 
                                                  Age_EE_exposure == 2 ~ "Early postnatal",
                                                  Age_EE_exposure == 3 ~ "Adolescent", 
                                                  Age_EE_exposure == 4 ~ "Adult",
                                                  Age_EE_exposure == 5 ~ "Unclear"),
                      Exposure_order = case_when(Exposure_order == 1 ~ "Stress first",
                                                      Exposure_order == 2 ~ "Enrichment first",
                                                      Exposure_order == 3 ~ "Concurrently", 
                                                      Exposure_order == 4 ~ "Unclear"),
                      Age_assay = case_when(Age_assay == 1 ~ "Early postnatal",
                                            Age_assay == 2 ~ "Adolescent",
                                            Age_assay == 3 ~ "Adult", 
                                            Age_assay == 4 ~ "Unclear"),
                      Sex = case_when(Sex == 1 ~ "Female", 
                                      Sex == 2 ~ "Male", 
                                      Sex == 3 ~ "Mixed", 
                                      Sex == 4 ~ "Unclear"),
                      Type_EE_exposure = case_when(Type_EE_exposure == 1 ~ "Nesting material",
                                                      Type_EE_exposure == 2 ~ "Objects",
                                                      Type_EE_exposure == 3 ~ "Cage complexity", 
                                                      Type_EE_exposure == 4 ~ "Wheel/trademill",
                                                      Type_EE_exposure == 5 ~ "Combination",
                                                      Type_EE_exposure == 6 ~ "Other", 
                                                      Type_EE_exposure == 7 ~ "Unclear"))

#making variance VCVs
VCV_E <- impute_covariance_matrix(vi = dat$lnRRV_E, cluster = dat$Study_ID, r = 0.5)
VCV_S <- impute_covariance_matrix(vi = dat$lnRRV_S, cluster = dat$Study_ID, r = 0.5)
VCV_ES <- impute_covariance_matrix(vi = dat$lnRRV_ES, cluster = dat$Study_ID, r = 0.5)

VCV_Ea <- impute_covariance_matrix(vi = dat$SMDV_E, cluster = dat$Study_ID, r = 0.5)
VCV_Sa <- impute_covariance_matrix(vi = dat$SMDV_S, cluster = dat$Study_ID, r = 0.5)
VCV_ESa <- impute_covariance_matrix(vi = dat$SMDV_ES, cluster = dat$Study_ID, r = 0.5)

#write.csv(dat, file = here("Data", 'Data_processed.csv'), row.names = TRUE)
```

# Modelling with lnRR

## Environmental enrichment

### Meta-analysis
Learning and memory are significantly improved when housed with environmnetal enrichment

``` {r, message = FALSE, warning = FALSE}
#dat <- read_csv(here("Data","Data_processed.csv"))

mod_E0 <- rma.mv(yi = lnRR_Ea, V = VCV_E, random = list(~1|Study_ID,
                                                          ~1|ES_ID,
                                                          ~1|Strain),
                 test = "t",
                 data = dat)
summary(mod_E0) 
i2_ml(mod_E0) 

orchard_plot(mod_E0, mod = "Int", xlab = "lnRR", alpha=0.4) +  # Orchard plot 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2)+ # confidence intervals
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_colour_manual(values = "darkorange")+ # change colours
  scale_fill_manual(values="darkorange")+ 
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 24), # change font sizes
        legend.title = element_text(size = 15),
        legend.text = element_text(size = 13)) 
```

### Meta-regression: uni-moderator {.tabset}

#### Type of assay
The type of learning/memory response

``` {r, message = FALSE, warning = FALSE,  fig.width=10, fig.height=7}
dat1 <- filter(dat, Type_assay %in% c("Recognition", "Habituation", "Conditioning"))
VCV_E1 <- impute_covariance_matrix(vi = dat1$lnRRV_E, cluster = dat$Study_ID, r = 0.5)

mod_E1 <- rma.mv(yi = lnRR_Ea, V = VCV_E1, mod = ~Type_assay-1, random = list(~1|Study_ID,
                                                                                  ~1|ES_ID,
                                                                                  ~1|Strain),
                 test = "t",
                 data = dat1)

summary(mod_E1)
r2_ml(mod_E1) 

Learning_E <- orchard_plot(mod_E1, mod = "Type_assay", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  xlim(-0.5, 2) + 
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

Learning_E
```

#### Learning vs Memory
Is the assay broadly measuring learning or memory? 

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
mod_E2 <-  rma.mv(yi = lnRR_Ea, V = VCV_E, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID,
                                                                                        ~1|ES_ID,
                                                                                        ~1|Strain),
                  test = "t",
                  data = dat)

summary(mod_E2) 
r2_ml(mod_E2) 

LvsM_E<- orchard_plot(mod_E2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  xlim(-0.5, 2) + 
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

LvsM_E
```

#### Type of reinforcement 
The type of cue used 

``` {r, message = FALSE, warning = FALSE,  fig.width=10, fig.height=7}

dat2 <- filter(dat, Type_reinforcement %in% c("Appetitive", "Aversive", "Not applicable"))
VCV_E2 <- impute_covariance_matrix(vi = dat2$lnRRV_E, cluster = dat2$Study_ID, r = 0.5)
mod_E3 <- rma.mv(yi = lnRR_Ea, V = VCV_E2, mod = ~ Type_reinforcement-1, random = list(~1|Study_ID,
                                                                                            ~1|ES_ID,
                                                                                            ~1|Strain),
                 test = "t",
                 data = dat2)

summary(mod_E3)
r2_ml(mod_E3) 

Reinforcement_E <-orchard_plot(mod_E3, mod = "Type_reinforcement", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  xlim(-0.5, 2) + 
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

Reinforcement_E
````

#### Social enrichment
Does EE also include a manipulation of social environment? Note that we excluded any studies that exclusively used social enrichment.s

``` {r, message = FALSE, warning = FALSE,  fig.width=10, fig.height=7}
dat5 <- filter(dat, EE_social %in% c("Social", "Non-social"))
VCV_E5 <- impute_covariance_matrix(vi = dat5$lnRRV_E, cluster = dat$Study_ID, r = 0.5)
  
mod_E4<- rma.mv(yi = lnRR_Ea, V = VCV_E5, mod = ~EE_social-1, random = list(~1|Study_ID, 
                                                                             ~1|ES_ID,
                                                                             ~1|Strain),
                test = "t",data = dat5)

summary(mod_E4)
r2_ml(mod_E4) 

Social_E <-orchard_plot(mod_E4, mod = "EE_social", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  xlim(-0.5, 2) + 
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
       text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

Social_E 
```

#### Exercise enrichment
Does the form of enrichment include exercise through a running wheel or treadmill?

``` {r, message = FALSE, warning = FALSE,  fig.width=10, fig.height=7}
mod_E5<- rma.mv(yi = lnRR_Ea, V = VCV_E, mod = ~EE_exercise-1, random = list(~1|Study_ID,
                                                                               ~1|ES_ID,
                                                                               ~1|Strain),
                test = "t",
                data = dat)

summary(mod_E5)
r2_ml(mod_E5) 
 
Exercise_E <-orchard_plot(mod_E5, mod = "EE_exercise", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  xlim(-0.5, 2) + 
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

Exercise_E
```

#### Age of enrichment
The age at which the individuals were exposed to environmental enrichment.
``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
dat6 <- filter(dat, Age_EE_exposure %in% c("Adult", "Adolescent"))
VCV_E6 <- impute_covariance_matrix(vi = dat6$lnRRV_E, cluster = dat6$Study_ID, r = 0.5)


mod_E6 <- rma.mv(yi = lnRR_Ea, V = VCV_E6, mod = ~Age_EE_exposure-1, random = list(~1|Study_ID,
                                                                                    ~1|ES_ID,
                                                                                    ~1|Strain),
                 test = "t",
                 data = dat6)

summary(mod_E6) 
r2_ml(mod_E6) 

Age_E<- orchard_plot(mod_E6, mod = "Age_EE_exposure", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  xlim(-0.5, 2) + 
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10))  

Age_E
```

###  Meta-regression: multi-moderator model

``` {r, message = FALSE, warning = FALSE, eval = FALSE}

# filter data so that all K < 5 are removed
dat_Efm <- dat %>%
  filter(Type_assay %in% c("Recognition", "Habituation", "Conditioning"),
         Type_reinforcement %in% c("Appetitive", "Aversive", "Not applicable"),
         EE_social %in% c("Social", "Non-social"), 
         Age_EE_exposure %in% c("Adult", "Adolescent"))

VCV_Efm <- impute_covariance_matrix(vi = dat_Efm$lnRRV_E, cluster = dat_Efm$Study_ID, r = 0.5)
                 
mod_Efm <- rma.mv(yi = lnRR_Sa, V = VCV_Efm, mod = ~Type_assay-1 + Learning_vs_memory + Type_reinforcement + EE_social + EE_exercise + Age_EE_exposure , random =   list(~1|Study_ID,
                                                                                    ~1|ES_ID,
                                                                                    ~1|Strain),
                    test = "t",
                 data = dat_Efm)
#summary(mod_Efm)
#r2_ml(mod_Efm) 

res_Efm <- dredge(mod_Efm, trace=2)
saveRDS(res_Efm, file = here("Rdata", "res_Efm.rds"))
# also saving the full model and data
saveRDS(mod_Efm, file = here("Rdata", "mod_Efm.rds"))
saveRDS(dat_Efm, file = here("Rdata", "dat_Efm.rds"))
```

```{r, message = FALSE, warning = FALSE}
dat_Efm <- readRDS(file = here("Rdata", "dat_Efm.rds"))
mod_Efm <- readRDS(file = here("Rdata", "mod_Efm.rds"))
res_Efm <- readRDS(file = here("Rdata", "res_Efm.rds"))
res_Efm2<- subset(res_Efm, delta <= 6, recalc.weights=FALSE)
importance(res_Efm2)
```

### Publication bias & sensitivity analysis

#### Publication bias tests
``` {r, message = FALSE, warning = FALSE}

# funnel plot
Funnel_E<-funnel(mod_Efm, xlab = "lnRR", ylab = "Standard Error")
Funnel_E
#year published was scaled previously  under stress PB

dat_Efm$sqrt_inv_e_n <- with(dat_Efm, sqrt(1/CC_n + 1/EC_n + 1/ES_n + 1/CS_n))

PB_MR_E<- rma.mv(lnRR_Sa, lnRRV_S, mods = ~1 + sqrt_inv_e_n +  Learning_vs_memory + Year_published + Type_assay + Type_reinforcement + EE_social + EE_exercise + Age_stress_exposure, random = list(~1|Study_ID,
                                                          ~1|ES_ID,
                                                          ~1|Strain), method = "REML", test = "t", 
    data = dat_Efm)

estimates_PB_MR_E<- estimates.CI(PB_MR_E)
estimates_PB_MR_E

#no effect of inv_effective sample size or year published
```

#### Leave-one-out analysis
``` {r, message = FALSE, warning = FALSE, eval = FALSE}
dat$Study_ID <- as.factor(dat$Study_ID)

LeaveOneOut_effectsize <- list()
for(i in 1:length(levels(dat$Study_ID))){
  LeaveOneOut_effectsize[[i]] <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, 
                                        random = list(~1 | Study_ID,~1| ES_ID, ~1 | Strain), 
                                        method = "REML", data = dat[dat$Study_ID
                                                                    != levels(dat$Study_ID)[i], ])}


# writing function for extracting est, ci.lb, and ci.ub from all models
est.func <- function(mod_E0){
  df <- data.frame(est = mod_E0$b, lower = mod_E0$ci.lb, upper = mod_E0$ci.ub)
  return(df)
}


#using dplyr to form data frame
MA_CVR_E <- lapply(LeaveOneOut_effectsize, function(x) est.func(x))%>% bind_rows %>% mutate(left_out = levels(dat$Study_ID))


saveRDS(MA_CVR_E,file = here("Rdata", "MA_CVR_E.rds"))
```

``` {r, message = FALSE, warning = FALSE}
#telling ggplot to stop reordering factors
MA_CVR_E <- readRDS(file = here("Rdata", "MA_CVR_E.rds"))

MA_CVR_E$left_out<- as.factor(MA_CVR_E$left_out)
MA_CVR_E$left_out<-factor(MA_CVR_E$left_out, levels = MA_CVR_E$left_out)


#plotting
leaveoneout_E <- ggplot(MA_CVR_E) +
  geom_hline(yintercept = 0, lty = 2, lwd = 1) +
  geom_hline(yintercept = mod_E0$ci.lb, lty = 3, lwd = 0.75, colour = "black") +
  geom_hline(yintercept = mod_E0$b, lty = 1, lwd = 0.75, colour = "black") +
  geom_hline(yintercept = mod_E0$ci.ub, lty = 3, lwd = 0.75, colour = "black") +
  geom_pointrange(aes(x = left_out, y = est, ymin = lower, ymax = upper)) +
  xlab("Study left out") + 
  ylab("lnRR, 95% CI") + 
  coord_flip() +
  theme(panel.grid.minor = element_blank())+
  theme_bw() + theme(panel.grid.major = element_blank()) +
  theme(panel.grid.minor.x = element_blank() ) +
  theme(axis.text.y = element_text(size = 6))

leaveoneout_E

dat$Study_ID <- as.integer(dat$Study_ID)
```

## Stress

### Intercept model
Learning and memory are significantly reduced due to stress. 
High heterogeneity

``` {r, message = FALSE, warning = FALSE}
mod_S0 <- rma.mv(yi = lnRR_Sa, V = VCV_S, random = list(~1|Study_ID,
                                                          ~1|ES_ID,
                                                          ~1|Strain),
                 test = "t", 
                 data = dat)

summary(mod_S0) 
i2_ml(mod_S0) 

orchard_plot(mod_S0, mod = "Int", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 24), # change font sizes
        legend.title = element_text(size = 15),
        legend.text = element_text(size = 13)) 
```

## Meta-regression

### Uni-moderator metaregression {.tabset}

#### Type of assay
The type of learning/memory response 

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
dat$Type_assay<-as.factor(dat$Type_assay)

VCV_S1 <- impute_covariance_matrix(vi = dat1$lnRRV_S, cluster = dat$Study_ID, r = 0.5)


mod_S1 <- rma.mv(yi = lnRR_Sa, V = VCV_S1, mod = ~Type_assay-1, random =   list(~1|Study_ID,
                                                                                    ~1|ES_ID,
                                                                                    ~1|Strain),
                 test = "t",
                 data = dat1)

summary(mod_S1)
r2_ml(mod_S1) 

Learning_S <-orchard_plot(mod_S1, mod = "Type_assay", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

Learning_S
```

#### Learning vs Memory
Is the assay broadly measuring learning or memory? 

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}

mod_S2 <-  rma.mv(yi = lnRR_Sa, V = VCV_S, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID,
                                                                                        ~1|ES_ID,
                                                                                        ~1|Strain),
                  test = "t",
                  data = dat)

summary(mod_S2) 
r2_ml(mod_S2) 

LvsM_S <- orchard_plot(mod_S2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
       text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

LvsM_S 
```

#### Type of reinforcement
The type of cue used 

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}

VCV_S2 <- impute_covariance_matrix(vi = dat2$lnRRV_S, cluster = dat$Study_ID, r = 0.5)

mod_S3 <- rma.mv(yi = lnRR_Sa, V = VCV_S2, mod = ~ Type_reinforcement-1, random = list(~1|Study_ID,
                                                                                            ~1|ES_ID,
                                                                                            ~1|Strain),
                 test = "t",
                 data = dat2)

summary(mod_S3)
r2_ml(mod_S3) 

Reinforcement_S <-orchard_plot(mod_S3, mod = "Type_reinforcement", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
       text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

Reinforcement_S
````

#### Type of stress
The type of stress manipulation 

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
dat3 <- filter(dat, Type_stress_exposure %in% c("Restraint", "Noise", "MS", "Combination"))
VCV_S3 <- impute_covariance_matrix(vi = dat3$lnRRV_S, cluster = dat3$Study_ID, r = 0.5)

mod_S4 <- rma.mv(yi = lnRR_Sa, V = VCV_S3, mod = ~Type_stress_exposure-1, random = list(~1|Study_ID,
                                                                                         ~1|ES_ID,
                                                                                         ~1|Strain),
                 test = "t",
                 data = dat3)
summary(mod_S4) 
r2_ml(mod_S4)

Stressor<- orchard_plot(mod_S4, mod = "Type_stress_exposure", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

Stressor
````

#### Age of stress
``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
VCV_S3a <- impute_covariance_matrix(vi = dat$lnRRV_S, cluster = dat$Study_ID, r = 0.5)

mod_S5 <-rma.mv(yi = lnRR_Sa, V = VCV_S3a, mod = ~Age_stress_exposure-1, random = list(~1|Study_ID,
                                                                                       ~1|ES_ID,
                                                                                       ~1|Strain),
                test = "t",
                data = dat)
summary(mod_S5) 
r2_ml(mod_S5) 

Age_S <- orchard_plot(mod_S5, mod = "Age_stress_exposure", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

Age_S 
```

#### Stess duration
How long was the stress applied for (chronic = every day for 7 days or more)?
This has the highest marginal R2

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
dat4 <- filter(dat, Stress_duration %in% c("Chronic", "Acute"))
VCV_S4 <- impute_covariance_matrix(vi = dat4$lnRRV_S, cluster = dat4$Study_ID, r = 0.5)

mod_S6 <-rma.mv(yi = lnRR_Sa, V = VCV_S4, mod = ~Stress_duration-1, random = list(~1|Study_ID,
                                                                                   ~1|ES_ID,
                                                                                   ~1|Strain),
                test = "t",
                data = dat4)
summary(mod_S6) 
r2_ml(mod_S6) 

Duration_S <- orchard_plot(mod_S6, mod = "Stress_duration", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

Duration_S 
```

###  Meta-regression: multi-moderator model
``` {r, message = FALSE, warning = FALSE, eval = FALSE}

#selecting moderator levels with k >=5
dat_Sfm <- dat %>%
  filter(Type_assay %in% c("Recognition", "Habituation", "Conditioning"),
         Type_reinforcement %in% c("Appetitive", "Aversive", "Not applicable"),
         Type_stress_exposure %in% c("Restraint", "Noise", "MS", "Combination"),
         Stress_duration %in% c("Chronic", "Acute"))

VCV_Sfm <- impute_covariance_matrix(vi = dat_Sfm$lnRRV_E, cluster = dat_Sfm$Study_ID, r = 0.5)
                 
mod_Sfm <- rma.mv(yi = lnRR_Sa, V = VCV_Sfm, mod = ~ Type_assay -1 + Learning_vs_memory + Type_reinforcement + Type_stress_exposure + Age_stress_exposure + Stress_duration, random =   list(~1|Study_ID,
                                                                                    ~1|ES_ID,
                                                                                    ~1|Strain),
                    test = "t",
                 data = dat_Sfm)
#summary(mod_Sfm)
#r2_ml(mod_Sfm) 

res_Sfm <- dredge(mod_Sfm, trace=2)
saveRDS(res_Sfm, file = here("Rdata", "res_Sfm.rds"))
# also saving the full model and data
saveRDS(mod_Sfm, file = here("Rdata", "mod_Sfm.rds"))
saveRDS(dat_Sfm, file = here("Rdata", "dat_Sfm.rds"))
```

```{r, message = FALSE, warning = FALSE}
dat_Sfm <- readRDS(file = here("Rdata", "dat_Sfm.rds"))
mod_Sfm <- readRDS(file = here("Rdata", "mod_Sfm.rds"))
res_Sfm <- readRDS(file = here("Rdata", "res_Sfm.rds"))
res_Sfm2<- subset(res_Sfm, delta <= 6, recalc.weights=FALSE)
importance(res_Sfm2) 
```

### Publication bias & sensitivity analysis

#### Publication bias

``` {r, message = FALSE, warning = FALSE}
# funnel plot
Funnel_S <- funnel(mod_Sfm, xlab = "lnRR", ylab = "Standard Error")
Funnel_S

#calculating inv effective sample size for use in full meta-regression
dat_Sfm$sqrt_inv_e_n <- with(dat_Sfm, sqrt(1/CC_n + 1/EC_n + 1/ES_n + 1/CS_n))

#time lag bias and eggers regression
dat_Sfm$c_Year_published <- as.vector(scale(dat_Sfm$Year_published, scale = F))

PB_MR_S<- rma.mv(lnRR_Sa, lnRRV_S, mods = ~1 + sqrt_inv_e_n +  c_Year_published + Type_assay +Learning_vs_memory + Type_reinforcement + Type_stress_exposure + Age_stress_exposure, random = list(~1|Study_ID,
                                                          ~1|ES_ID,
                                                          ~1|Strain), 
                 method = "REML", 
                 test = "t", 
                 data = dat_Sfm,
                  control=list(optimizer="optim", optmethod="Nelder-Mead"))

estimates_PB_MR_S<- estimates.CI(PB_MR_S)
estimates_PB_MR_S
#no effect of inv_effective sample size or year published
```

#### Leave-one-out sensitivity analysis

``` {r, message = FALSE, warning = FALSE, eval = FALSE}
dat$Study_ID <- as.factor(dat$Study_ID)

LeaveOneOut_effectsize <- list()
for(i in 1:length(levels(dat$Study_ID))){
  LeaveOneOut_effectsize[[i]] <- rma.mv(yi = lnRR_Sa, V = lnRRV_S, 
                                        random = list(~1 | Study_ID,~1| ES_ID, ~1 | Strain), 
                                        method = "REML", data = dat[dat$Study_ID
                                                                    != levels(dat$Study_ID)[i], ])}


# writing function for extracting est, ci.lb, and ci.ub from all models
est.func <- function(mod_E0){
  df <- data.frame(est = mod_E0$b, lower = mod_E0$ci.lb, upper = mod_E0$ci.ub)
  return(df)
}


#using dplyr to form data frame
MA_CVR_S <- lapply(LeaveOneOut_effectsize, function(x) est.func(x))%>% bind_rows %>% mutate(left_out = levels(dat$Study_ID))

saveRDS(MA_CVR_S,file = here("Rdata", "MA_CVR_S.rds"))

```

``` {r, message = FALSE, warning = FALSE}
MA_CVR_S <- readRDS(file = here("Rdata", "MA_CVR_S.rds"))

#telling ggplot to stop reordering factors
MA_CVR_S$left_out<- as.factor(MA_CVR_S$left_out)
MA_CVR_S$left_out<-factor(MA_CVR_S$left_out, levels = MA_CVR_S$left_out)

#plotting
leaveoneout_S <- ggplot(MA_CVR_S) +
  geom_hline(yintercept = 0, lty = 2, lwd = 1) +
  geom_hline(yintercept = mod_S0$ci.lb, lty = 3, lwd = 0.75, colour = "black") +
  geom_hline(yintercept = mod_S0$b, lty = 1, lwd = 0.75, colour = "black") +
  geom_hline(yintercept = mod_S0$ci.ub, lty = 3, lwd = 0.75, colour = "black") +
  geom_pointrange(aes(x = left_out, y = est, ymin = lower, ymax = upper)) +
  xlab("Study left out") + 
  ylab("lnRR, 95% CI") + 
  coord_flip() +
  theme(panel.grid.minor = element_blank())+
  theme_bw() + theme(panel.grid.major = element_blank()) +
  theme(panel.grid.minor.x = element_blank() ) +
  theme(axis.text.y = element_text(size = 6))

leaveoneout_S

dat$Study_ID <- as.integer(dat$Study_ID)
```


## Interaction of stress and EE

### Intercept
Enriched and stress animals are better at learning and memory. 

``` {r, message = FALSE, warning = FALSE}
mod_ES0 <- rma.mv(yi = lnRR_ESa, V = VCV_ES, random = list(~1|Study_ID,
                                                             ~1|ES_ID,
                                                             ~1|Strain),
                  test = "t", 
                  data = dat)

summary(mod_ES0) 
i2_ml(mod_ES0) 

orchard_plot(mod_ES0, mod = "Int", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 24), # change font sizes
        legend.title = element_text(size = 15),
        legend.text = element_text(size = 13)) 
````

#### Intercept with outlier removed

``` {r, message = FALSE, warning = FALSE}

dat_outliers <- dat %>%
  filter(ES_ID != 88)

VCV_ES_outliers <- impute_covariance_matrix(vi = dat_outliers$lnRRV_E, cluster = dat_outliers$Study_ID, r = 0.5)

mod_ESoutlier <- rma.mv(yi = lnRR_ESa, V = VCV_ES_outliers, random = list(~1|Study_ID,
                                                             ~1|ES_ID,
                                                             ~1|Strain),
                  test = "t", 
                  data = dat_outliers)

summary(mod_ESoutlier)

orchard_plot(mod_ESoutlier, mod = "Int", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 24), # change font sizes
        legend.title = element_text(size = 15),
        legend.text = element_text(size = 13))   
```

## Meta-regression

### Uni-moderator meta-regression {.tabset}

#### Type of assay
The type of learning/memory response 

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
VCV_ES1 <- impute_covariance_matrix(vi = dat1$lnRRV_ES, cluster = dat$Study_ID, r = 0.5)

mod_ES1 <- rma.mv(yi = lnRR_ESa, V = VCV_ES1, mod = ~Type_assay-1, random = list(~1|Study_ID,
                                                                                    ~1|ES_ID,
                                                                                    ~1|Strain),
                  test = "t",
                  data = dat1)

summary(mod_ES1)
r2_ml(mod_ES1) 

Learning_ES <- orchard_plot(mod_ES1, mod = "Type_assay", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 3, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        axis.text.x = element_text(size = 10), # change font sizes
        axis.text.y = element_text(size = 10),
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 7)) 

Learning_ES
```

#### Learning vs Memory
Is the assay broadly measuring learning or memory? 

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
mod_ES2 <-  rma.mv(yi = lnRR_ESa, V = VCV_ES, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID, 
                                                                                           ~1|ES_ID,
                                                                                           ~1|Strain),
                   test = "t",
                   data = dat)

summary(mod_ES2) 
r2_ml(mod_ES2) 

LvsM_ES <- orchard_plot(mod_ES2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 3, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        axis.text.x = element_text(size = 10), # change font sizes
        axis.text.y = element_text(size = 10),
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 7)) 

LvsM_ES 
```

#### Type of reinforcement
The type of conditioning used

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
VCV_ES2 <- impute_covariance_matrix(vi = dat2$lnRRV_ES, cluster = dat2$Study_ID, r = 0.5)

mod_ES3 <- rma.mv(yi = lnRR_ESa, V = VCV_ES2, mod = ~ Type_reinforcement-1, random = list(~1|Study_ID,
                                                                                               ~1|ES_ID,
                                                                                               ~1|Strain),
                  test = "t",
                  data = dat2)

summary(mod_ES3)
r2_ml(mod_ES3) 

Reinforcement_ES <- orchard_plot(mod_ES3, mod = "Type_reinforcement", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 3, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        axis.text.x = element_text(size = 10), # change font sizes
        axis.text.y = element_text(size = 10),
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 7)) 

Reinforcement_ES 
````

#### Type of stress
The type of stress manipulation 

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
VCV_ES3 <- impute_covariance_matrix(vi = dat3$lnRRV_ES, cluster = dat3$Study_ID, r = 0.5)
mod_ES4 <- rma.mv(yi = lnRR_ESa, V = VCV_ES3, mod = ~Type_stress_exposure-1, random = list(~1|Study_ID,
                                                                                            ~1|ES_ID,
                                                                                            ~1|Strain),
                  test = "t",
                  data = dat3)
summary(mod_ES4)
r2_ml(mod_ES4)

Stressor_ES <- orchard_plot(mod_ES4, mod = "Type_stress_exposure", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 3, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        axis.text.x = element_text(size = 10), # change font sizes
        axis.text.y = element_text(size = 10),
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 7))  

Stressor_ES 
````

#### Age of stress
The age at which the individuals were exposed to the stressor.
``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
mod_ES5 <-rma.mv(yi = lnRR_ESa, V = VCV_ES, mod = ~Age_stress_exposure-1, random = list(~1|Study_ID,
                                                                                          ~1|ES_ID,
                                                                                          ~1|Strain),
                 test = "t",
                 data = dat)
summary(mod_ES5) 
r2_ml(mod_ES5) 

Age_stress_ES<-orchard_plot(mod_ES5, mod = "Age_stress_exposure", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 3, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
       axis.text.x = element_text(size = 10), # change font sizes
        axis.text.y = element_text(size = 10),
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 7)) 

Age_stress_ES
```

#### Stress duration
How long was the stress applied for (chronic = every day for 7 days or more)?
This has the highest marginal R2 (currentl nearly 43%) - need to redo without outlier

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
VCV_ES4 <- impute_covariance_matrix(vi = dat4$lnRRV_ES, cluster = dat4$Study_ID, r = 0.5)

mod_ES6 <-rma.mv(yi = lnRR_ESa, V = VCV_ES4, mod = ~Stress_duration-1, random = list(~1|Study_ID,
                                                                                      ~1|ES_ID,
                                                                                      ~1|Strain),
                 test = "t",
                 data = dat4)
summary(mod_ES6) 
r2_ml(mod_ES6) 


Duration_ES<- orchard_plot(mod_ES6, mod = "Stress_duration", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 3, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        axis.text.x = element_text(size = 10), # change font sizes
        axis.text.y = element_text(size = 10),
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 7)) 

Duration_ES
```

#### Social enrichment
Does EE also include a manipulation of social environment (i.e., number of individuals in EE relative to control)? 

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
VCV_ES5 <- impute_covariance_matrix(vi = dat5$lnRRV_ES, cluster = dat5$Study_ID, r = 0.5)
mod_ES7<- rma.mv(yi = lnRR_ESa, V = VCV_ES5, mod = ~EE_social-1, random = list(~1|Study_ID,
                                                                                ~1|ES_ID,
                                                                                ~1|Strain),
                 test = "t",
                 data = dat5)

summary(mod_ES7)
r2_ml(mod_ES7) 

Social_ES <- orchard_plot(mod_ES7, mod = "EE_social", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 3, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        axis.text.x = element_text(size = 10), # change font sizes
        axis.text.y = element_text(size = 10),
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 7)) 

Social_ES
```

#### Exercise enrichment
Does the form of enrichment include exercise through a running wheel or treadmill? 

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
mod_ES8<- rma.mv(yi = lnRR_ESa, V = VCV_ES, mod = ~EE_exercise-1, random = list(~1|Study_ID, 
    ~1|ES_ID,
    ~1|Strain),
     test = "t",
     data = dat)

summary(mod_ES8)
r2_ml(mod_ES8) 

Exercise_ES <- orchard_plot(mod_ES8, mod = "EE_exercise", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 3, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
       axis.text.x = element_text(size = 10), # change font sizes
        axis.text.y = element_text(size = 10),
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 7)) 

Exercise_ES
```

#### Order to treatment exposure
What order were animals exposed to stress and EE

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
mod_ES9 <- rma.mv(yi = lnRR_ESa, V = VCV_ES, mod = ~Exposure_order -1, random = list(~1|Study_ID, 
    ~1|ES_ID,
    ~1|Strain),
     test = "t",
     data = dat)

summary(mod_ES9)
r2_ml(mod_ES9)

Order_ES <- orchard_plot(mod_ES9, mod = "Exposure_order", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 3, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        axis.text.x = element_text(size = 10), # change font sizes
        axis.text.y = element_text(size = 10),
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 7)) 

Order_ES 
```

#### Age of enrichment
What age were individuals exposed to EE
``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
VCV_ES6 <- impute_covariance_matrix(vi = dat6$lnRRV_ES, cluster = dat6$Study_ID, r = 0.5)

mod_ES10 <- rma.mv(yi = lnRR_ESa, V = VCV_ES6, mod = ~Age_EE_exposure-1, random = list(~1|Study_ID,
                                                                                    ~1|ES_ID,
                                                                                    ~1|Strain),
                 test = "t",
                 data = dat6)

summary(mod_ES10) 
r2_ml(mod_ES10) 

Age_enrichment_ES <- orchard_plot(mod_ES10, mod = "Age_EE_exposure", xlab = "lnRR", alpha=0.4) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
  geom_point(aes(fill = name),  size = 3, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
       axis.text.x = element_text(size = 10), # change font sizes
        axis.text.y = element_text(size = 10),
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 7)) 

Age_enrichment_ES
```

###  Meta-regression: multi-moderator model

``` {r, message = FALSE, warning = FALSE, eval = FALSE}
dat_ESfm <- dat %>%
  filter(Type_assay %in% c("Recognition", "Habituation", "Conditioning"),
         Type_reinforcement %in% c("Appetitive", "Aversive", "Not applicable"),
         EE_social %in% c("Social", "Non-social"),
         Age_EE_exposure %in% c("Adult", "Adolescent"),
         Type_stress_exposure %in% c("Restraint", "Noise", "MS", "Combination"),
         Stress_duration %in% c("Chronic", "Acute"), 
         Age_stress_exposure %in% c("Prenatal", "Early postnatal", "Adult"))

VCV_ESfm <- impute_covariance_matrix(vi = dat_ESfm$lnRRV_ES, cluster = dat_ESfm$Study_ID, r = 0.5)
                 
mod_ESfm <- rma.mv(yi = lnRR_Sa, V = VCV_ESfm, mod = ~Type_assay-1 + Learning_vs_memory + Type_reinforcement + EE_social + EE_exercise + Age_EE_exposure + Type_stress_exposure + Age_stress_exposure + Stress_duration + Exposure_order, random =   list(~1|Study_ID,
                                                                                    ~1|ES_ID,
                                                                                    ~1|Strain),
                    test = "t",
                 data = dat_ESfm)
#summary(mod_ESfm)
#r2_ml(mod_ESfm) 


res_ESfm <- dredge(mod_ESfm, trace=2)
saveRDS(res_ESfm, file = here("Rdata", "res_ESfm.rds"))
# also saving the full model and data
saveRDS(mod_ESfm, file = here("Rdata", "mod_ESfm.rds"))
saveRDS(dat_ESfm, file = here("Rdata", "dat_ESfm.rds"))
```


```{r}
dat_ESfm <- readRDS(file = here("Rdata", "dat_ESfm.rds"))
mod_ESfm <- readRDS(file = here("Rdata", "mod_ESfm.rds"))
res_ESfm <- readRDS(file = here("Rdata", "res_ESfm.rds"))
res_ESfm2<- subset(res_ESfm, delta <= 6, recalc.weights=FALSE)
importance(res_ESfm2) 
```

### Publication bias & sensitivity analysis

#### Publication bias

``` {r}
Funnel_ES<-funnel(mod_ESfm, xlab = "lnRR", ylab = "Standard Error")
Funnel_ES
#year published was scaled previously under stress PB

dat_ESfm$sqrt_inv_e_n <- with(dat_ESfm, sqrt(1/CC_n + 1/EC_n + 1/ES_n + 1/CS_n))

PB_MR_ES<- rma.mv(lnRR_ESa, lnRRV_ES, mods = ~1 + sqrt_inv_e_n +  Year_published + Learning_vs_memory + Type_assay + Type_reinforcement + EE_social + EE_exercise + Age_stress_exposure, random = list(~1|Study_ID,
                                                          ~1|ES_ID,
                                                          ~1|Strain), method = "REML", test = "t", 
    data = dat_ESfm)

estimates_PB_MR_ES<- estimates.CI(PB_MR_ES)
estimates_PB_MR_ES

#no effect of inv_effective sample size or year published
```

#### Leave-one-out analysis

``` {r, message = FALSE, warning = FALSE, eval = FALSE}
dat$Study_ID <- as.factor(dat$Study_ID)

LeaveOneOut_effectsize <- list()
for(i in 1:length(levels(dat$Study_ID))){
  LeaveOneOut_effectsize[[i]] <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, 
                                        random = list(~1 | Study_ID,~1| ES_ID, ~1 | Strain), 
                                        method = "REML", data = dat[dat$Study_ID
                                                                    != levels(dat$Study_ID)[i], ])}


# writing function for extracting est, ci.lb, and ci.ub from all models
est.func <- function(mod_E0){
  df <- data.frame(est = mod_E0$b, lower = mod_E0$ci.lb, upper = mod_E0$ci.ub)
  return(df)
}


#using dplyr to form data frame
MA_CVR_ES <- lapply(LeaveOneOut_effectsize, function(x) est.func(x))%>% bind_rows %>% mutate(left_out = levels(dat$Study_ID))

saveRDS(MA_CVR_ES, ,file = here("Rdata", "MA_CVR_ES.rds"))

```

``` {r, message = FALSE, warning = FALSE}
MA_CVR_ES<- readRDS(here("Rdata", "MA_CVR_ES.rds"))

#telling ggplot to stop reordering factors
MA_CVR_ES$left_out<- as.factor(MA_CVR_ES$left_out)
MA_CVR_ES$left_out<-factor(MA_CVR_ES$left_out, levels = MA_CVR_ES$left_out)

#plotting
leaveoneout_ES <- ggplot(MA_CVR_ES) +
  geom_hline(yintercept = 0, lty = 2, lwd = 1) +
  geom_hline(yintercept = mod_E0$ci.lb, lty = 3, lwd = 0.75, colour = "black") +
  geom_hline(yintercept = mod_E0$b, lty = 1, lwd = 0.75, colour = "black") +
  geom_hline(yintercept = mod_E0$ci.ub, lty = 3, lwd = 0.75, colour = "black") +
  geom_pointrange(aes(x = left_out, y = est, ymin = lower, ymax = upper)) +
  xlab("Study left out") + 
  ylab("lnRR, 95% CI") + 
  coord_flip() +
  theme(panel.grid.minor = element_blank())+
  theme_bw() + theme(panel.grid.major = element_blank()) +
  theme(panel.grid.minor.x = element_blank() ) +
  theme(axis.text.y = element_text(size = 6))

leaveoneout_ES

dat$Study_ID <- as.integer(dat$Study_ID)
```

### Combined orchard plot

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
mod_list1 <- list(mod_E0, mod_S0, mod_ES0)

mod_res1 <- lapply(mod_list1, function(x) mod_results(x, mod = "Int"))

merged1 <- submerge(mod_res1[[3]], mod_res1[[2]],  mod_res1[[1]], mix = T)
merged1$mod_table$name <- factor(merged1$mod_table$name, levels = c("Intrcpt1", 
    "Intrcpt2", "Intrcpt3"), 
    labels = rev(c("Enrichment ME", "Stress ME", "Interaction")))

merged1$data$moderator <- factor(merged1$data$moderator, levels = c("Intrcpt1", 
    "Intrcpt2", "Intrcpt3"), 
    labels = rev(c("Enrichment ME", "Stress ME", "Interaction")))

orchard1<- orchard_plot(merged1, mod = "Int", xlab = "lnRR", angle = 0) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals 
  xlim(-2,4.5) +
  geom_point(aes(fill = name),  size = 4, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling +
  scale_colour_manual(values = c("#00AEEF","#00A651","#ED1C24"))+ # change colours
  scale_fill_manual(values=c("#00AEEF","#00A651","#ED1C24"))+
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

orchard1
```

## 'Pairwise' effect sizes {.tabset}

### Enrichment relative to control

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}

VCV_E20 <- impute_covariance_matrix(vi = dat$lnRRV_E2, cluster = dat$Study_ID, r = 0.5)

#Model doesn't converge with VCV
mod_E20 <- rma.mv(yi = lnRR_E2a, V = VCV_E20, random = list(~1|Study_ID,
                                                             ~ 1|Strain,
                                                             ~1|ES_ID),
                 test = "t", 
                 data = dat, 
                 control=list(optimizer="optim", optmethod="Nelder-Mead"))

summary(mod_E20) 
i2_ml(mod_E20) 

orchard_plot(mod_E20, mod = "Int", xlab = "lnRR")
```

### Stress relative to control

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
VCV_S20 <- impute_covariance_matrix(vi = dat$lnRRV_S2, cluster = dat$Study_ID, r = 0.5)

mod_S20 <- rma.mv(yi = lnRR_S2a, V = VCV_S20, random = list(~1|Study_ID, 
                                                             ~ 1|Strain,
                                                             ~1|ES_ID),
                 test = "t",
                 data = dat)

summary(mod_S20) 
i2_ml(mod_S20) 
 
orchard_plot(mod_S20, mod = "Int", xlab = "lnRR")
```

### Enrichment + stress relative to control

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
VCV_ES20 <- impute_covariance_matrix(vi = dat$lnRRV_ES2, cluster = dat$Study_ID, r = 0.5)

mod_ES20 <- rma.mv(yi = lnRR_ES2a, V = VCV_ES20, random = list(~1|Study_ID,
                                                                ~ 1|Strain,
                                                                ~1|ES_ID),
                 test = "t",
                 data = dat)
summary(mod_ES20) 
i2_ml(mod_ES20) 

orchard_plot(mod_ES20, mod = "Int", xlab = "lnRR")
```

### Enrichment + stress relative to stress

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
VCV_E30 <- impute_covariance_matrix(vi = dat$lnRRV_E3, cluster = dat$Study_ID, r = 0.5)

mod_E30 <- rma.mv(yi = lnRR_E3a, V = VCV_E30, random = list(~1|Study_ID,
                                                             ~ 1|Strain,
                                                             ~1|ES_ID),
                  test = "t",
                  data = dat)
summary(mod_E30) 
i2_ml(mod_E30) 

orchard_plot(mod_E30, mod = "Int", xlab = "lnRR")
```

### Enrichment + stress relative to enrichment

``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}
VCV_S30 <- impute_covariance_matrix(vi = dat$lnRRV_S3, cluster = dat$Study_ID, r = 0.5)

mod_S30 <- rma.mv(yi = lnRR_S3a, V = VCV_S30, random = list(~1|Study_ID,
                                                             ~ 1|Strain,
                                                             ~1|ES_ID),
                  test = "t",
                  data = dat,
                   control=list(optimizer="optim", optmethod="Nelder-Mead"))
summary(mod_S30) 
i2_ml(mod_S30) 

orchard_plot(mod_S30, mod = "Int", xlab = "lnRR")
```

## Combined orchard plot
``` {r, message = FALSE, warning = FALSE, fig.width=10, fig.height=7}

mod_list2 <- list(mod_S30, mod_E30, mod_ES20, mod_S20, mod_E20) #rearranged the order so that it matches intext results

mod_res2 <- lapply(mod_list2, function(x) mod_results(x, mod = "Int"))

merged2 <- submerge(mod_res2[[1]], mod_res2[[2]],  mod_res2[[3]], mod_res2[[4]],  mod_res2[[5]], mix = T)

merged2$mod_table$name <- factor(merged2$mod_table$name, levels = c("Intrcpt1", 
    "Intrcpt2", "Intrcpt3", "Intrcpt4", "Intrcpt5"), 
    labels = rev(c("EC/CC", "CS/CC", "ES/CC", "ES/CS", "ES/EC")))

merged2$data$moderator <- factor(merged2$data$moderator, levels = c("Intrcpt1", 
    "Intrcpt2", "Intrcpt3", "Intrcpt4", "Intrcpt5"), 
    labels = rev(c("EC/CC", "CS/CC", "ES/CC", "ES/CS", "ES/EC")))

orchard2 <- orchard_plot(merged2, mod = "Int", xlab = "lnRR", angle = 0) + 
  geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
  geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals 
  xlim(-2,4.5) +
  geom_point(aes(fill = name),  size = 4, shape = 21)+ # mean estimate
  scale_size_continuous(range = c(1, 7))+ # change point scaling
  scale_colour_manual(values = c("#7B81BE","#D7DF23","#F37158","#75CBF2","#97D2B4"))+ # change colours
  scale_fill_manual(values=c("#7B81BE","#D7DF23","#F37158","#75CBF2","#97D2B4"))+
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
        text = element_text(size = 15), # change font sizes
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) 

orchard2
```

## Panel of 'focal' ES and 'pairwise' ES orchard plots
``` {r, message = FALSE, warning = FALSE}


p1 <- orchard1 + orchard2 +  plot_annotation(tag_levels = 'A')
p1

#saved as PDF: 6 x 15 inches
```


## Panel of meta-regressions {.tabset}

### Environmental enrichment
``` {r, message = FALSE, warning = FALSE,  fig.width=10, fig.height=7}
#Enrichment
E_mod <- (LvsM_E + Learning_E + Reinforcement_E)/ (Age_E + Exercise_E + Social_E) +  plot_annotation(tag_levels = 'A')

E_mod
#saved as pdf 10 x 15 inches
```

### Stress
``` {r, message = FALSE, warning = FALSE,  fig.width=10, fig.height=7}
S_mod <- (LvsM_S + Learning_S + Reinforcement_S) / (Age_S + Stressor + Duration_S) + plot_annotation(tag_levels = 'A')

S_mod
#saved as pdf 10 x 15 inches
```

### Interaction
``` {r, message = FALSE, warning = FALSE,  fig.width=10, fig.height=7}
ES_mod <- plot_grid(LvsM_ES, Learning_ES, Reinforcement_ES, Age_enrichment_ES, Age_stress_ES, Order_ES, Exercise_ES, Social_ES, Stressor_ES, Duration_ES,
  labels = "AUTO", ncol = 5)

ES_mod
#saved as 10 x 20 inches
```

## Panel of funnel plots
``` {r, message = FALSE, warning = FALSE,  fig.width=12, fig.height=6,echo = FALSE}
# EE

pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,4,0.1,0))
funnel(mod_Sfm, xlab = "lnRR", ylab = "Standard Error",
        xlim = c(-2,2),
        ylim = c(0,1.05))
A <- recordPlot()
invisible(dev.off())

# Stress

pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,4,0.1,0))
funnel(mod_Sfm, xlab = "lnRR", ylab = "Standard Error",
        xlim = c(-2,2),
        ylim = c(0,1.05))
B <- recordPlot()
invisible(dev.off())

# Interaction
pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,4,0.1,0))
funnel(mod_ESfm, xlab = "lnRR", ylab = "Standard Error",
        xlim = c(-2,2),
        ylim = c(0,1.05))
C <- recordPlot()
invisible(dev.off())

# putting together
funnels <- (ggdraw(A) + ggdraw(B) + ggdraw(C)) + plot_annotation(tag_levels = 'A')
funnels


png(file = here("figs", "funnels.png"))

dev.off()

```

# Modelling with SMD

## Environmental Enrichment

### Intercept model

``` {r, message = FALSE, warning = FALSE}

# TODO 
#Why does the funnel plot look so strange and why is I2 higher?

mod_E0a <- rma.mv(yi = SMD_Ea, V = VCV_Ea, random = list(~1|Study_ID,
                                                         ~1|ES_ID, 
                                                         ~1|Strain),
                 test = "t",
                 data = dat)
summary(mod_E0a)
i2_ml(mod_E0a)
funnel(mod_E0a, yaxis="seinv")

# VCV matrix
VCV_Efm <- impute_covariance_matrix(vi = dat_Efm$lnRRV_E, cluster = dat_Efm$Study_ID, r = 0.5)

mod_E0b <- rma.mv(yi = SMD_Ea, V = VCV_Efm, mod = ~Type_assay-1 + Learning_vs_memory-1 + Type_reinforcement-1 + EE_social-1 + EE_exercise-1 + Age_EE_exposure-1, random = list(~1|Study_ID,~1|ES_ID,~1|Strain),
                 test = "t",
                 data = dat_Efm)

funnel(mod_E0b, yaxis="seinv")
```

## Stress

### Intercept model

``` {r, message = FALSE, warning = FALSE}
mod_S0a <- rma.mv(yi = SMD_Sa, V = VCV_Sa, random = list(~1|Study_ID,
                                                         ~1|ES_ID,
                                                         ~1|Strain),
                test = "t",
                data = dat)
summary(mod_S0a) 
i2_ml(mod_S0a) 
funnel(mod_S0a, yaxis="seinv")

# VCV matrix
VCV_Sfm <- impute_covariance_matrix(vi = dat_Sfm$lnRRV_E, cluster = dat_Sfm$Study_ID, r = 0.5)

#assessing funnel on model model
mod_S0b <- rma.mv(yi = SMD_Sa, V = VCV_Sfm, mod = ~ Type_assay -1 + Learning_vs_memory + Type_reinforcement + Type_stress_exposure + Age_stress_exposure + Stress_duration, random =   list(~1|Study_ID,
                                                                                    ~1|ES_ID,
                                                                                    ~1|Strain),
                    test = "t",
                 data = dat_Sfm)
funnel(mod_S0b, yaxis="seinv")
```

## Interaction

### Intercept model

``` {r, message = FALSE, warning = FALSE}
mod_ES0a <- rma.mv(yi = SMD_ESa, V = VCV_ESa, random = list(~1|Study_ID,
                                                            ~1|ES_ID,
                                                            ~1|Strain),
                  test = "t",
                  data = dat)
summary(mod_ES0a)
i2_ml(mod_ES0a) 
funnel(mod_ES0a, yaxis="seinv")

# VCV matrix
VCV_ESfm <- impute_covariance_matrix(vi = dat_ESfm$lnRRV_E, cluster = dat_ESfm$Study_ID, r = 0.5)

mod_ES0b <- rma.mv(yi = SMD_Sa, V = VCV_ESfm, mod = ~Type_assay-1 + Learning_vs_memory + Type_reinforcement + EE_social + EE_exercise + Age_EE_exposure + Type_stress_exposure + Age_stress_exposure + Stress_duration + Exposure_order, random =   list(~1|Study_ID,
                                                                                    ~1|ES_ID,
                                                                                    ~1|Strain),
                    test = "t",
                 data = dat_ESfm)

funnel(mod_ES0b, yaxis="seinv")
```

Social enrichment
Does EE also include a manipulation of social environment? Note that we excluded any studies that exclusively used social enrichment.s